a forensic investigation of single human hair …i a forensic investigation of single human hair...

i

A Forensic Investigation of Single Human Hair Fibres

using FTIR-ATR Spectroscopy and Chemometrics

A thesis submitted as partial fulfilment

of the requirements

for the degree of

Doctor of Philosophy (PhD)

By

Paul M.J. Barton

BAppSc (Hons)

Based on research carried out in the

School of Physical and Chemical Sciences/Discipline of Chemistry

Queensland University of Technology

Under the supervision of

Adjunct Associate Professor Serge Kokot

Associate Professor Godwin Ayoko

Queensland University of Technology, Brisbane February 2011

ii

STATEMENT OF ORIGINAL AUTHORSHIP

The work contained in this thesis has not been submitted for a degree of diploma at any

other higher education institution. To the best of my knowledge and belief, the thesis

contains no material previously published or written by another person except where

due reference is made.

___________________________ ___________________________

Paul M.J Barton

iii

ACKNOWLEDGEMENTS

First and foremost I would like to dedicate this work to and acknowledge my late

Grandfather, Earnest Benjamin Moya (1918-2006), a proud Cherokee Native American.

I strongly believe I inherited my determination and intellect from him. I also would like

to thank my mother, Linda Maureen Moya, who has always supported and protected me

from harm‟s way and life‟s misfortunes. I would like to thank my high school teachers,

Mr Ewan Toombes (Year 9 Science Teacher) who triggered my love of Science and

Mrs Sarah Howes (Year 11 and 12 Chemistry Teacher) who furthered my motivation in

Chemistry and guided me to University. I strongly believe in my High School‟s Motto

(Glenala State High School) “Believe and Achieve”. However, without the mention of

the next two mentors, I believe that I may not have reached the pinnacle of education

that I have accomplished. Dr Serge Kokot, my principal supervisor, a man who I hold

up in the highest respect, has always believed in and supported me through to the

completion of my education. Dr Godwin Ayoko, my associate supervisor, another man

who I highly regard as a mentor and motivator, also believed that I could complete my

PhD candidature. I sincerely thank my fellow undergraduate and postgraduate

colleagues, Adrian Fuchs, Adrian Friend, Ben Morrow, Dylan Nagle, and Kenneth

Nuttall. The King of Science, Albert Einstein, gave me the inspiration to study Science

in general.

I would also like to acknowledge the funding that I have received from the University

namely the QUT BLUPRINT Award Scholarship and the Write-Up Scholarship,

without which the completion of the PhD candidature would have been extremely

difficult. Honourable mentions should also extend to Associate Professor Fredericks

and Dr Llew Rintoul for teaching and sharing their knowledge of Fourier Transform

Infrared Spectroscopy. Lastly, I would thank all the people that donated their hair fibres

for this research project, especially those from Sugarland, Texas, U.S.A. These fibres

have allowed the continued research into the forensic analysis of hair for matching and

discrimination. I hope and envisage this dissertation will be an important and novel

contribution to the field of forensic science, inspiring others from disadvantaged

backgrounds as did the former Inala High student, now the former Premier of QLD, Mr

Wayne Goss.

iv

ABSTRACT

Human hair fibres are ubiquitous in nature and are found frequently at crime scenes

often as a result of exchange between the perpetrator, victim and/or the surroundings

according to Locard‟s Principle. Therefore, hair fibre evidence can provide important

information for crime investigation. For human hair evidence, the current forensic

methods of analysis rely on comparisons of either hair morphology by microscopic

examination or nuclear and mitochondrial DNA analyses. Unfortunately in some

instances the utilisation of microscopy and DNA analyses are difficult and often not

feasible. This dissertation is arguably the first comprehensive investigation aimed to

compare, classify and identify the single human scalp hair fibres with the aid of FTIR-

ATR spectroscopy in a forensic context.

Spectra were collected from the hair of 66 subjects of Asian, Caucasian and African (i.e.

African-type). The fibres ranged from untreated to variously mildly and heavily

cosmetically treated hairs. The collected spectra reflected the physical and chemical

nature of a hair from the near-surface particularly, the cuticle layer. In total, 550 spectra

were acquired and processed to construct a relatively large database. To assist with the

interpretation of the complex spectra from various types of human hair, Derivative

Spectroscopy and Chemometric methods such as Principal Component Analysis (PCA),

Fuzzy Clustering (FC) and Multi-Criteria Decision Making (MCDM) program;

Preference Ranking Organisation Method for Enrichment Evaluation (PROMETHEE)

and Geometrical Analysis for Interactive Aid (GAIA); were utilised.

FTIR-ATR spectroscopy had two important advantages over to previous methods: (i)

sample throughput and spectral collection were significantly improved (no physical

flattening or microscope manipulations), and (ii) given the recent advances in FTIR-

ATR instrument portability, there is real potential to transfer this work‟s findings

seamlessly to on-field applications.

The “raw” spectra, spectral subtractions and second derivative spectra were compared to

demonstrate the subtle differences in human hair. SEM images were used as

corroborative evidence to demonstrate the surface topography of hair. It indicated that

the condition of the cuticle surface could be of three types: untreated, mildly treated and

v

treated hair. Extensive studies of potential spectral band regions responsible for

matching and discrimination of various types of hair samples suggested the

1690-1500 cm-1

IR spectral region was to be preferred in comparison with the

commonly used 1750-800 cm-1

. The principal reason was the presence of the highly

variable spectral profiles of cystine oxidation products (1200-1000 cm-1

), which

contributed significantly to spectral scatter and hence, poor hair sample matching. In

the preferred 1690-1500 cm-1

region, conformational changes in the keratin protein

attributed to the α-helical to β-sheet transitions in the Amide I and Amide II vibrations

and played a significant role in matching and discrimination of the spectra and hence,

the hair fibre samples.

For gender comparison, the Amide II band is significant for differentiation. The results

illustrated that the male hair spectra exhibit a more intense β-sheet vibration in the

Amide II band at approximately 1511 cm-1

whilst the female hair spectra displayed

more intense α-helical vibration at 1520-1515cm-1

. In terms of chemical composition,

female hair spectra exhibit greater intensity of the amino acid tryptophan (1554 cm-1

),

aspartic and glutamic acid (1577 cm-1

). It was also observed that for the separation of

samples based on racial differences, untreated Caucasian hair was discriminated from

Asian hair as a result of having higher levels of the amino acid cystine and cysteic acid.

However, when mildly or chemically treated, Asian and Caucasian hair fibres are

similar, whereas African-type hair fibres are different.

In terms of the investigation‟s novel contribution to the field of forensic science, it has

allowed for the development of a novel, multifaceted, methodical protocol where

previously none had existed. The protocol is a systematic method to rapidly investigate

unknown or questioned single human hair FTIR-ATR spectra from different genders

and racial origin, including fibres of different cosmetic treatments. Unknown or

questioned spectra are first separated on the basis of chemical treatment i.e. untreated,

mildly treated or chemically treated, genders, and racial origin i.e. Asian, Caucasian and

African-type. The methodology has the potential to complement the current forensic

analysis methods of fibre evidence (i.e. Microscopy and DNA), providing information

on the morphological, genetic and structural levels.

vi

TABLE OF CONTENTS

STATEMENT OF ORIGINAL AUTHORSHIP ......................................................... ii

ACKNOWLEDGEMENTS .......................................................................................... iii

ABSTRACT .................................................................................................................... iv

TABLE OF CONTENTS .............................................................................................. vi

LIST OF FIGURES ...................................................................................................... xii

LIST OF TABLES ..................................................................................................... xxiv

ABBREVIATIONS ................................................................................................... xxvii

1.0 INTRODUCTION .................................................................................................... 1

1.1 Prologue to the Investigation ........................................................................................... 1 1.2 Human Hair Fibres ........................................................................................................... 6

1.2.1 The Morphology of Human Hair Fibres .......................................................... 7 1.2.1.1 The Cuticle ................................................................................................. 7

1.2.1.2 The Cortex .................................................................................................. 9

1.2.1.3 The Medulla ............................................................................................. 10

1.2.1.4 Melanin Pigment and Greying of Hair ................................................... 10

1.2.2 The Chemical Structure of Human Hair Fibres ............................................. 11 1.2.2.1 α- Keratin Proteins .................................................................................. 11

1.2.2.2 Bonding Mechanisms in Keratin – Covalent and Non-covalent Forces . 13

1.2.3 The Chemical Process of Bleaching Human Hair Fibres .............................. 14 1.2.3.1 The Mechanism of Bleaching ................................................................... 15

1.2.3.2 The Disulphide (S-S) Cleavage Mechanism ............................................ 15

1.2.4 Chemical Process of Hair Dyeing and Colouring .......................................... 16 1.2.4.1 Temporary Colourants ............................................................................. 16

1.2.4.2 Semi-Permanent Colourants .................................................................... 16

1.2.4.3 Permanent or Oxidative Dyeing .............................................................. 17

1.2.5 Permanent Waving and Straightening of Human Hair Fibres ....................... 17 1.2.5.1 Chemical Process of Permanent Waving ................................................ 18

1.2.6 Hair Straightening .......................................................................................... 19 1.2.7 Photo-oxidative Bleaching ............................................................................. 19

vii

1.2.8 Oxidation of Hair with Chlorine .................................................................... 20 1.2.9 Physical Properties of the α-Keratin Fibre ..................................................... 20

1.2.9.1 Mechanical Properties of the Keratin Fibre ............................................ 20

1.2.9.2 The Keratin-Water System ....................................................................... 21

1.2.10 The Effects of Mechanical or Physical Processes on Human Hair Fibres ... 22 1.2.10.1 Effects of Shampooing, Conditioning, Combing, Grooming and Towel

Drying .................................................................................................................. 22

1.2.10.2 Effects of Thermal Treatments on Human Hair Fibres ......................... 23

1.3 Forensic Science: Trace Physical Evidence ..................................................... 24

1.3.1 Forensic Fibre Evidence ................................................................................ 25

1.4 Current Methods of Forensic Fibre Analysis with the use of Microscopy and

DNA Analysis ............................................................................................................ 27

1.4.1 Macroscopic Analysis.....................................................................................27

1.4.1 Microscopy .................................................................................................... 27 1.4.1.1 Optical Light Microscopy and Stereomicroscopy ................................... 27

1.4.1.2 Scanning Electron Microscopy ................................................................ 28

1.4.2 Fibre Evidence from Burial Scenes ............................................................... 29 1.4.2.1 Burial of Hair Fibres ............................................................................... 29

1.4.2.2 Environmental Weathering of Fibre Evidence ........................................ 30

1.4.3 DNA Analysis ................................................................................................ 31 1.4.3.1 DNA Analysis of Human Hair Fibres ...................................................... 31

1.4.3.2 Mitochondrial DNA ................................................................................. 32

1.5 Vibrational Spectroscopy ................................................................................... 34

1.5.1 Infrared Spectroscopy .................................................................................... 37 1.5.1.1 Infrared Absorptions ................................................................................ 37

1.5.1.2 Infrared Modes of Vibration .................................................................... 38

1.5.2 The Fourier Transform Infrared Spectrometer .............................................. 40 1.5.2.1 Fourier-Transformation........................................................................... 42

1.5.2.2 Advantages ............................................................................................... 42

1.5.3 Forensic Investigations of Human Hair Fibres using FT-IR Spectroscopy ... 43 1.5.3.1 Applications of Chemometrics to Forensic Science ................................ 45

1.5.3.2 Previous Investigations using FT-IR Spectroscopy and Chemometrics .. 47

1.5.3.3 Limitations to the Previous Investigations............................................... 47

1.5.4 Fourier Transform Infrared Spectroscopy - Attenuated Total Reflectance ... 49 1.5.4.1 Previous Investigations of Human Hair Fibres Utilising FTIR-ATR

Spectroscopy with the aid of Chemometrics and SEM ........................................ 53

1.5.5 Alternative FT-IR Sampling Techniques for Analysing α-Keratin Fibres .... 55 1.5.5.1 FT-IR Photoacoustic Spectroscopy (PAS) of Human Hair Fibres .......... 55

1.5.5.2 FT-Raman Spectroscopy of Human Hair Fibres ..................................... 56

viii

1.5.6 Derivative Spectroscopy ................................................................................ 57 1.5.6.1 Properties of Derivative Profiles ............................................................. 59

1.5.6.2 Generating Derivative Spectra: The Savitzky-Golay Method ................ 62

1.6 Aims and Objectives ........................................................................................... 64

2.0 EXPERIMENTAL: MATERIALS AND METHODS ....................................... 66

2.1 Collection of Fibre Samples ............................................................................... 66 2.2 SEM Analysis ...................................................................................................... 66 2.3 Cleaning Methodology ........................................................................................ 67

2.3.1 Revised IAEA Method for Cleaning Hair Fibres .......................................... 67

2.4 FTIR-ATR Spectroscopy ................................................................................... 68 2.5 Spectral Processing ............................................................................................. 69

2.5.1 Derivative Spectroscopy ................................................................................ 70

2.6 Pre-processing of the Raw Data Matrix and Chemometric Analysis ............ 70

2.6.1 Variance Scaling ............................................................................................ 71 2.6.1.1 Double Centring ...................................................................................... 71

2.6.1.2 Standardisation ........................................................................................ 72

2.6.1.3 Autoscaling .............................................................................................. 72

2.6.2 Chemometric Analysis ................................................................................... 73 2.6.3 Multi-criteria Decision Making (MCDM) ..................................................... 73

2.7 Chemometrics ...................................................................................................... 73

2.7.1 Chemometrics and Forensic Science ............................................................. 74 2.7.2 Principal Component Analysis (PCA) ........................................................... 75 2.7.3 Classification ................................................................................................. 76

2.7.3.2 Fuzzy Clustering (FC) ............................................................................. 78

2.7.4 Multi-criteria Decision Making Techniques (MCDM) ................................. 79 2.7.4.1 PROMETHEE I and II Multivariate Techniques ..................................... 80

2.7.4.2 GAIA ........................................................................................................ 88

3.0 CUTICLE SURFACE TOPOGRAPHY AND FTIR-ATR SPECTRAL

CHARACTERISTICS OF THE MORPHOLOGICAL-CHEMICAL

STRUCTURE OF HUMAN HAIR FIBRES .............................................................. 90

3.1 Morphological Characteristics of the Cuticle Surface Topography of Human

Hair Fibres Involving SEM ...................................................................................... 94

3.1.1 Comparison of Chemically Untreated and Cosmetically Treated Human Hair

Fibres ...................................................................................................................... 94 3.1.1.1 SEM Analysis of Non-Treated Hair Fibres .............................................. 95

3.1.1.2 SEM Analysis of Different Cosmetically Treated Hair Fibres ................ 97

3.2 Structural Elucidation of -Keratin Hair Fibres using FTIR-ATR

Spectroscopy ............................................................................................................ 102

ix

3.2.1 Comparison of Chemically Untreated and Cosmetically Treated Fibres .... 102 3.2.1.1 Secondary Structure Conformations and Vibrational Modes of the

Peptide Bond ...................................................................................................... 102

3.2.1.2 FTIR-ATR Spectral Analysis of Untreated Hair Fibres ........................ 103

3.2.1.3 Spectral Analysis of Cosmetically Treated Hair Fibres ........................ 108

3.2.2 Analysis of Difference FTIR-ATR Spectra of Human Hair Fibres between

Gender ................................................................................................................... 120 3.2.2.1 Spectral Differences between Genders of each Race ............................ 120

3.3 The Application of Derivative Spectroscopy for Interpretation of FTIR-ATR

Spectra of Single Hair Fibres ................................................................................. 125

3.3.1. Optimisation of the Savitzky-Golay Method for Second Derivative Analysis

.............................................................................................................................. 125 3.3.2. Assessment of Typical Second Derivative FTIR-ATR Spectra of Untreated

α-Keratin Fibres .................................................................................................... 129 3.2.3. Assessment of Typical Second Derivative FTIR-ATR Chemically Treated α-

Keratin Spectra ..................................................................................................... 136 3.3 Chapter Conclusions ....................................................................................... 146

4.0 FORENSIC PROTOCOL FOR ANALYSING HUMAN HAIR FIBRES

USING FTIR-ATR SPECTROSCOPY WITH THE AID OF CHEMOMETRICS

AND MCDM ............................................................................................................... 147

4.1 The Protocol – A Systematic Approach to Hair Fibre Analysis ................... 148 4.2 Optimisation of the Proposed Forensic Protocol for Spectroscopic Analysis of

Human Hair Fibres with the aid of Chemometrics ............................................. 152

4.2.1 Spectral Regions and Fibre Discrimination ................................................. 153 4.2.1.1 Spectral Range 1750-800 cm

-1 ............................................................... 153

4.2.1.2 PROMETHEE and GAIA Analysis: 1750-800 cm-1

Spectral Range .... 169

4.2.1.3 Conclusions: 1750-800 cm-1

Database .................................................. 178

4.2.2 Investigation of the Alternative Spectral Regions ....................................... 179 4.2.2.1 Spectral Range - 1690-1200 cm

-1 .......................................................... 179

4.2.2.2 Chemometric Analysis of Single Human Hair Fibres using Alternative

Spectral Regions - 1690-1500 cm-1

.................................................................... 189

4.2.3 Chemometric Analysis of Further Alternative Spectral Regions of Keratin

FTIR-ATR and Second Derivative Spectra .......................................................... 197

4.3 Chapter Conclusions............................................................................................. 197

5.0 APPLICATIONS OF THE FORENSIC PROTOCOL AS AN

IDENTIFICATION PROCEDURE FOR SINGLE HUMAN HAIR FIBRES ..... 201

5.1 Principles of the Forensic Protocol .................................................................. 201

x

5.2 African-type Hair Fibres .................................................................................. 204

5.2.1 Physical and Chemical characteristics of African-type hair fibres: ............. 204 5.2.2 FTIR-ATR Spectroscopic-Chemometric Analysis of African-type Hair Fibres

.............................................................................................................................. 205 5.2.2.1 Comparison of the 1750-800 cm

-1 and 1690-1500 cm

-1 regions ........... 206

5.2.2.2 MCDM Analysis of African-type Hair Fibres ........................................ 213

5.3.1 Incorporation of the African-type Hair IR Spectra to the Protocol ............. 220 5.3.1.1 Chemometric Analysis of the Entire (3 Races) Database ...................... 220

5.3 Gender: Male vs. Female Hair Fibres ............................................................. 229

5.3.1 Gender Differences between Untreated, Mildly Treated and Chemically

Treated Fibres ....................................................................................................... 229 5.3.1.1 Untreated Hair Fibres ........................................................................... 229

5.3.1.2 Mildly Treated Hair Fibres .................................................................... 233

5.3.1.3 Chemically Treated Hair Fibres ............................................................ 242

5.4 Race: Asian, Caucasian and African-type Hair Fibres ................................. 247

5.4.1 Racial Spectral differences between Female Hair Fibres ............................ 249 5.4.1.1 Untreated Female Hair Fibres .............................................................. 249

5.4.1.2 Chemically Treated Female Hair Fibres ............................................... 253

5.4.2 Racial spectral differences between Male Hair Fibre Spectra ..................... 258 5.4.2.1. Mildly Treated Male Hair Fibres ......................................................... 260

5.4.2.2. Chemically Treated Male Hair Fibres .................................................. 265

5.5 Potential Extension of the Forensic Protocol ................................................. 270 5.6 Chapter Conclusions......................................................................................... 271

6.0 CONCLUSIONS AND FUTURE INVESTIGATIONS .................................... 274

6.1 Concluding Remarks ........................................................................................ 274

6.1.1 Conclusions of Chapter 3 ............................................................................. 274 6.1.2 Conclusions of Chapter 4 ............................................................................. 276 6.1.3 Conclusions to Chapter 5 ............................................................................. 277

6.2 Future Investigations ........................................................................................ 279

7.0 REFERENCES ...................................................................................................... 282

Appendix I – Data on Subjects - Forensic Protocol ................................................. 299

Appendix I (Continued) - Hair Profile Survey for Forensic Investigation ............ 302

xi

Appendix II – Fuzzy Clustering (p = 1.2) 3-cluster model 1750-800 cm-1

............. 303

Appendix III–Fuzzy Clustering (p = 1.2) 4-cluster Model 1750-800 cm-1

............. 305

Appendix IV–Fuzzy Clustering (p = 1.2) 3 Cluster 1690-1200 cm-1

....................... 309

Appendix V–Fuzzy Clustering (p = 1.2) 3-cluster Model 1690-1500 cm-1

............. 311

Appendix VI – FC (p=1.2) African-type Hair Fibres 1750-800 cm-1

...................... 314

Appendix VII – FC (p = 1.2) African-type Hair Fibres 1690-1500 cm-1

................ 317

Appendix VIII – FC (p = 1.2) Mildly Treated Database 1690-1500 cm-1

.............. 319

Appendix IX – FC (p =1.2) Treated Hair Database 1690-1500 cm-1

...................... 322

Appendix X – Alternative Spectral Regions for the Proposed Forensic Protocol

(Continued from Chapter 4) ...................................................................................... 324

4.2.3.1 Chemometric Analysis of Single Human Hair Fibres using Alternative

Spectral Regions - 1690-1360 cm-1

.................................................................... 324

4.2.3.2 Second Derivative Keratin FTIR-ATR Spectra 1750-800 cm-1

Region

........................................................................................................................ ...329


Region

.......................................................................................................................... .333

xii

LIST OF FIGURES

Figure 1.1:

A schematic diagram of a human hair fibre illustrating

the morphological features starting from the external

Cuticle, Cortex, Macrofibril, Microfibril down to the -

Helical Protein.

7

Figure 1.2: An illustration of the cross-section of a developed

cuticle cell.

8

Figure 1.3: The condensation reaction of amino acids. 11

Figure 1.4: Molecular structure of the amino acid Cystine.

13

Figure 1.5: Hydrogen bonding between the amide and carbonyl

groups in the -keratin structure.

14

Figure 1.6: Scheme of the S-S cleavage mechanism for the

bleaching process.

15

Figure 1.7: Reaction scheme between the disulphide bond and a

mercaptan where K represents the Keratin chain and R

represents the amino R-group side chains

18

Figure 1.8: C-S fission mechanism of -keratin by photo-oxidative

bleaching.

19

Figure 1.9: A histograph indicating the relationship between the

frequency with which different types of trace evidence

occurs in criminal cases.

27

Figure 1.10

Absorption of energy for a vibration where the

molecule is promoted from state E0 to state E1 and the

molecule in the higher vibrational state (E1) dropping to

the lower vibrational state (E0) emitting radiation of ΔE.

36

Figure 1.11 :

Localised vibrations of the methylene group

highlighting the symmetric and anti-symmetric

stretches, and the bending/scissoring, rocking, twisting

and wagging vibrations respectively.

38

xiii

Figure 1.12:

Figure 1.13

Modes of Vibrations for the Amide I, Amide II and

Amide III bands respectively for -keratin protein.

A schematic diagram of the Michelson Interferometer.

39

41

Figure 1.14: Total Internal Reflection in Attenuated Total

Reflectance Spectroscopy.

50

Figure 1.15:

Figure 1.16:

An evanescent wave that is produced upon Total

Internal Reflection that eventually penetrates the

sample.

A spectral comparison of -keratin spectra using FTIR

Micro-spectroscopy (blue line) and FTIR-ATR

Spectroscopy (pink line).

50

54

Figure 2.1:

Figure 2.2:

Figure 2.3:

Figure 2.4:

Figure 3.1:

Figure 3.2:

Figure 3.3:

Figure 3.4:

Figure 3.5:

A photograph of the MEGANSON Ultrasonic

Disintegrator that was used to sonicate the fibres for this

study.

A photograph of the NEXUS 870 FT-IR E.S.P

Spectrometer fitted with a Diamond-ATR Smart

Accessory. The arrows indicate the positions of the

pressure tower and the diamond crystal.

A preference function P(d).

Function H(d).

SEM image of an untreated Asian female hair fibre.

SEM image of an untreated Caucasian male hair fibre.

SEM image of an untreated African hair fibre.

SEM image of the tip end of a treated African male hair

fibre that has formed a knot possibly caused due by the

effects of grooming.

SEM image of the same treated African male hair fibre

(Figure 3.4) which has been subject to a “pink”

moisturising lotion. This image illustrates lifting and

chipping of the cuticle scales.

67

68

81

82

95

96

97

98

99

xiv

Figure 3.6:

Figure 3.7:

Figure 3.8:

Figure 3.9:

Figure 3.10:

Figure 3.11:

Figure 3.12:

SEM image of a permanently dyed Asian female hair

fibre.

SEM image of a bleached and semi-permanently dyed

Caucasian female hair fibre that receives constant sun

exposure.

A selection of 12 typical untreated FTIR-ATR spectra

of human hair fibres from male (M) and female (F)

donors of the major races: Caucasian (C), Asian (A) and

African-type (N). (Note: The vertical lines designate

the vibrational assignment and peak position of each

functional group/molecular fragment. The arrows

indicate the direction of the vibration).

A selection of 10 typical and 2 atypical chemically

treated FTIR-ATR spectra of human hair fibres from

male (M) and female (F) donors of the major races:

Caucasian (C), Asian (A) and African-type (N).

(a) FTIR-ATR spectrum of NF5 suspected to contain a

hair activator on the surface, (b) FTIR-ATR spectrum of

NF5 after cleaning of the surface and (c) the subtraction

of (b) - (a) yielding the IR spectrum of the suspicious

material.

Resultant FTIR-ATR spectral subtraction of the

chemically treated NM7 spectrum minus the cleaned

version of the fibre revealing the characteristic bands of

a long-chain silo-oxane resin used in hair gel and

hairspray formulations.

A subtraction FTIR-ATR spectrum of the average of

untreated Caucasian female No. 1 (peak maxima) minus

the average of untreated Caucasian male No. 3(peak

minima).

100

101

104

109

115

119

121

xv

Figure 3.13:

Figure 3.14:

Figure 3.15:

Figure 3.16:

Figure 3.17:

Figure 3.18:

Figure 3.19:

Figure 4.1:


untreated Asian female No. 17 (peak maxima) minus

the average of untreated Asian male No. 20 (peak

minima).


untreated African-type female No. 21 (peak maxima)

minus the average of untreated African-type male No. 1

(peak minima).

Second derivative FTIR-ATR spectra of an untreated

Caucasian female fibre using a two degree polynomial

and comparing different number of smoothing points (5,

7, 9 and 11). Increase in smoothing points shows that

resolution between the bands decreases. Thus a 2o

polynomial with 5-points was selected.

Typical second derivative FTIR-ATR spectrum of hair

from a Caucasian female untreated No. 1(CFUN1).

A comparison of six typical (alleged according to hair

history) untreated second, derivative FTIR-ATR spectra

of hair from both male (M) and female (F) of the

Caucasian (C), Asian (A) and African-type (N) races.

A comparison of four typical mildly treated, second

derivative FTIR-ATR spectra of hair from both male

(M) and female (F) of the Caucasian (C), Asian (A) and

African-type (N) races.

A comparison of seven typical chemically treated,

second derivative FTIR-ATR spectra of hair from both

male (M) and female (F) of the Caucasian (C), Asian

(A) and African-type (N) races.

The proposed forensic protocol for the analysis of

unknown hair fibres using FTIR spectroscopy and

Chemometrics with the inclusion of the novel African-

type group (green).

123

124

128

130

131

139

142

149

xvi

Figure 4.2:

Figure 4.3:

Figure 4.4:

Figure 4.5:

Figure 4.6:

Figure 4.7:

Figure 4.8:

PCA scores plot of PC1 (75.7 %) vs. PC2 (10.8 %) of

the untreated fibres (blue), the chemically treated fibres

(pink) and the entire African-type fibre database (green)

using the traditional spectral region between

1750-800 cm-1

.


the untreated fibres (blue) and the chemically treated

fibres (pink) of Caucasian and Asian fibres between

1750-800 cm-1

.

Re-classified PCA scores plot of PC1 (74.8 %) vs. PC2

(14.4 %) of the untreated fibres (blue), the chemically

treated fibres (pink), the mild treated fibres (green) and

the „fuzzy‟ samples (black) of the Caucasian and Asian

fibres.

Re-classified PCA scores plot of PC1 (74.8 %) vs. PC2

(14.4 %) of the untreated fibres (blue), the chemically

treated fibres (pink) and the mildly treated fibres

(green) of the Caucasian and Asian hair fibres between

1750-800 cm-1

.


the untreated fibres (blue), the chemically treated fibres

(pink), the mildly physically treated fibres (turquoise),

and the mild chemically treated fibres (light green) of

the Caucasian and Asian hair fibres between

1750-800 cm-1

based on a four class FC model.

PC1 Loadings plot of the chemically treated and mildly

treated fibres (positive loadings), and the untreated and

mildly treated fibres (negative loadings) between

1750-800 cm-1

region.

PC2 Loadings plot of the mildly treated hair fibres

(positive loadings), and the untreated and chemically

treated fibres (negative loadings) 1750-800 cm-1

.

155

156

162

163

164

166

168

xvii

Figure 4.9:

Figure 4.10:

Figure 4.11:

Figure 4.12:

Figure 4.13:

Figure 4.14:

GAIA analysis of the 176 spectra for the Caucasian and

Asian hair fibre database between 1750-800 cm-1

; ■

untreated fibres, ■ chemically treated fibres, ■ mildly

treated hair fibres, ● pi (Π) decision-making axis, and ■

Original PC1 and PC2 criteria using a Gaussian

preference function.

GAIA analysis of the 164 spectra for the Caucasian and

Asian hair fibre database between 1750-800 cm-1

using

a 4-cluster model; ▲untreated fibres, ■ chemically

treated fibres, ■ mild chemical treatment hair fibres, ■

mild physical treatment hair fibres, ● pi (Π) decision-

making axis, and ■ Original PC1, PC2 and PC3 criteria

using a Gaussian preference function.

PCA scores plot of PC1 (79.5 %) vs. PC2 (8.3 %) of the

untreated fibres (blue), chemically treated fibres (pink),

mildly treated fibres (green) using the alternate spectral

region between 1690-1200 cm-1

.

PC1 Loadings plot of the chemically treated fibres

(positive loadings) and the untreated and mildly treated

fibres (negative loadings) between

1690-1200 cm-1

.

PC2 Loadings of the untreated and chemically treated

fibres (positive loadings) and mildly treated fibres

(negative loadings) between 1690 -1200 cm-1

.

GAIA analysis of the 212 spectra for the

1690-1200 cm-1

hair fibre database; ▲ untreated fibres,

■ chemically treated fibres, ■ mildly treated hair fibres,

● pi (Π) decision-making axis, and ■ Original PC1 and

PC2 criterion variables using a Gaussian preference

function.

173

177

180

182

183

188

xviii

Figure 4.15:

Figure 4.16:

Figure 4.17:

Figure 4.18:

Figure 5.1:

Figure 5.2:

Figure 5.3:


the untreated fibres (blue), mildly treated fibres (green)

and the chemically treated fibres (pink) using the

alternate spectral region between 1690-1500 cm-1

.

PC1 Loadings plot of the untreated and mildly treated

fibres (positive loadings) and the chemically treated

fibres (negative loadings) between

1690-1500 cm-1

.

PC2 Loadings plot of the untreated and chemically

treated fibres (positive loadings) and the mildly treated

fibres (negative loadings) between 1690-1500 cm-1

.

GAIA analysis of the 209 spectra for the

1690-1500 cm-1

hair fibre database; ▲ untreated fibres,

■ chemically treated fibres, ■ mildly treated hair fibres,

● pi (Π) decision-making axis, and ■ Original PC1 and

PC2 criterion variables using a Gaussian preference

function.

PC1 vs. PC2 scores plot of untreated♦, mildly treated▲

and chemically treated fibres■ for the African-type hair

fibres between 1750 - 800 cm-1

.

PC1 vs. PC2 scores plot of untreated♦, mildly treated▲

and chemically treated fibres■ for the African-type hair

fibres between 1690-1500 cm-1

.

PC1 vs. PC2 scores plot of the African-type 1750-800

cm-1

spectral database based on a 4-cluster FC model

illustrating the untreated♦, mild physical treatment▲,

mild chemical treatment■ and chemically treated■

spectral objects.

190

191

191

196

207

207

209

xix

Figure 5.4:

Figure 5.5:

Figure 5.6:

Figure 5.7:

Figure 5.8:

Figure 5.9

PC1 vs. PC2 scores plot of the African-type

1690-1500 cm-1

spectral database based on a 4-cluster

FC model illustrating the untreated■, mild physical

treatment▲, mild chemical treatment and chemically

treated♦ spectral objects.

PC1 Loadings plot of the chemically treated and mildly

treated African-type hair fibres (positive loadings), and

the untreated and mildly treated African-type fibres

(negative loadings) between 1750-800 cm-1

IR region.

PC1 Loadings plot of the untreated and mildly treated

African-type hair fibres (positive loadings) and the

chemically treated African-type hair fibres (negative

loadings) between 1690-1500 cm-1

IR region.

GAIA analysis of the 111 spectra for the African-type

hair fibre database between 1750-800 cm-1

; ▲untreated

fibres, ■ chemically treated fibres, ■ mildly treated hair

fibres, ● pi (Π) decision-making axis, and ■ Original

PC1 and PC2 criteria using a Gaussian preference

function.

GAIA analysis of the 124 spectra for the African-type

hair fibre database between 1690-1500 cm-1

; ■untreated

fibres, ■ chemically treated fibres, ■ mildly treated hair

fibres, ● pi (Π) decision-making axis, and ■ PC1 and

PC2 criteria using a Gaussian preference function.

PCA scores plot of the 1690 -1500 cm-1

IR Database;

Caucasian and Asian untreated fibres●, chemically

treated fibres■, with the inclusion of the untreated

African-type untreated♦ and chemically treated■

African-type spectral objects.

210

211

212

218

219

221

xx

Figure 5.10:

Figure 5.11:

Figure 5.12:

Figure 5.13:

Figure 5.14:

Figure 5.15:

PCA scores plot of PC1 vs. PC2 of the

1690 -1500 cm-1

IR Database. Caucasian and Asian

untreated fibres●, chemically treated fibres■, mildly

treated fibres▲ and African-type untreated♦, mildly

treated▲ and chemically treated■ hair fibres.

GAIA analysis of the 257 spectra for the Entire (3

Race) IR database between 1690-1500 cm-1

; ■untreated

fibres, ■ untreated African-type fibres, ■ chemically

treated fibres, ■ chemically treated African-type fibres,

■ mildly treated hair fibres, ■mildly treated African-

type fibres, ● pi (Π) decision-making axis, and ■

Original PC1, PC2 and PC3criteria using a Gaussian


PCA scores plot of PC1 vs. PC2 of the 1750-800 cm-1

IR Database. Caucasian and Asian untreated fibres●,

chemically treated fibre■, mildly treated fibres▲, and

African-type untreated♦, mildly treated▲ and

chemically treated■ spectral objects.

PCA scores plot of PC1 vs. PC2 of the Untreated Hair

Fibre Spectral Database illustrating the separation of

untreated African-type Male No.1♦ from untreated

Female■ spectral objects along the PC2 axis.

PC2 Loadings plot of the untreated African-type Male

No. 1 fibres (positive loadings) and the untreated

Female fibres (negative loadings).

GAIA analysis of the 39 spectra for the Untreated hair

fibre database; ■ Male untreated fibres, ■ Female

untreated fibres, ● pi (Π) decision-making axis, and

Original ■ PC1 and PC2 criteria using a Gaussian


222

226

227

230

231

234

xxi

Figure 5.16:

Figure 5.17:

Figure 5.18:

Figure 5.19:

Figure 5.20:

Figure 5.21:

PCA scores plot of PC1 vs. PC2 of the Mildly Treated

Hair Fibre Spectral Database illustrating the separation

of mildly treated male♦ from mildly treated female♦

spectral objects.

PCA scores plot of PC1 vs. PC2 of the Mildly Treated

Hair Fibre Spectral Database illustrating the separation

of mildly treated male♦ from mildly treated female♦ and

male mild physical■ and female mild physical ■ from

female mild chemical▲ and male mild chemical▲.

PC2 Loadings plot of the Mildly Treated spectral

database showing the separation of male-female mild

physical-chemical from mildly treated female and male

fibres on the PC2 axis.

GAIA analysis of the 121 spectra for the Mildly Treated

hair fibre database; ■ Male mildly treated fibres, ■

Female mildly treated fibres,■ Male mild physical,

■Female mild physical, ■Male mild chemical, ■

Female mild chemical, ● pi (Π) decision-making axis,

and ■ PC1 and PC2 criteria.

PCA scores plot of PC1 vs. PC2 of the Chemically

Treated Hair Fibre Spectral Database illustrating the

separation of treated male■, African-type male treated■

African-type female treated▲ from treated female♦ on

the PC2 axis.

GAIA analysis of the 109 spectra for the Chemically

Treated hair fibre database; ■ Male mildly treated

fibres, ■ Female mildly treated fibres,■ African-type

male, ■ African-type female,, ● pi (Π) decision-making

axis, and ■ PC1, PC2 and PC3 criteria.

236

236

237

241

243

246

xxii

Figure 5.22:

Figure 5.23:

Figure 5.24:

Figure 5.25:

Figure 5.26:

Figure 5.27:

PCA scores plot of PC1 vs. PC2 of the Untreated

Female spectral database which illustrates the

separation of untreated Caucasian female♦ spectra from

untreated Asian female■ spectra on the PC1 axis.

PC1 Loadings plot of the Untreated Female spectral

database. The Amide I and II vibrational bands

(positive loadings) correlate to the untreated Asian

female spectral objects whilst the β-sheet, υa(CO2) and

Tryptophan bands (negative loadings) are associated

with the untreated Caucasian female spectral objects.

GAIA analysis of the 29 spectra for the Untreated

Female hair fibre database; ■ Caucasian Female

untreated spectral objects, ■ Asian Female untreated

spectral objects, ● pi (Π) decision-making axis, and ■

Original PC1, PC2 and PC3 criteria using a Gaussian


PCA scores plot of PC1 vs. PC2 of the Female Treated

spectral database illustrating the segregation of Asian■,

Caucasian♦ and African-type▲ spectral objects.

PC2 Loadings plot of the FemaleTreated database

where the treated Asian spectral objects (positive

loadings) are separated from the treated Caucasian and

African-type spectral objects (negative loadings).

GAIA analysis of the 35 spectra for the Chemically

Treated Female hair fibre database; ▲ Caucasian

female treated objects, ■ Asian female treated objects,

African-type female objects■, ● pi (Π) decision-making

axis, and ■ Original PC1 and PC2 criteria using a

Gaussian preference function.

250

251

254

255

256

259

xxiii

Figure 5.28:

Figure 5.29:

Figure 5.30:

Figure 5.31:

Figure 5.32:

Figure 5.33:

Figure 5.34:

PCA scores plot of PC1 vs. PC2 of the Male Mildly

Treated spectral database illustrating the separation of

African-type male objects▲ from Asian■ and

Caucasian♦ objects on the PC2 axis.

PC2 Loadings plot of the Male Mildly treated database

which illustrates spectral variables that separate

African-type male mildly treated (positive loadings)

from Asian and Caucasian (negative loadings) mildly

treated fibres.

GAIA analysis of the 92 spectra for the Male Mildly

Treated hair fibre database; ■ Caucasian male mildly

treated objects, ■ Asian male mildly treated objects,

African-type male mildly treated objects■, ● pi (Π)

decision-making axis, and ■ Original PC1, PC2 and


PCA scores plot of PC1 vs. PC2 of the Male

Chemically Treated Database which illustrates the

separation of Asian■ and Caucasian♦ from African-

type▲ spectral objects on the PC2 axis.

PC2 Loadings plot of the male treated spectral database

illustrating the variables which separate the Asian and

Caucasian (positive loadings) from the African-type

(negative loadings) spectral objects.

GAIA analysis of the 41 spectra for the Male

Chemically Treated hair fibre database; ■ Caucasian

male treated objects, ■ Asian male treated objects,

African-type male treated objects■, ● pi (Π) decision-

making axis, and ■ Original PC1, PC2 and PC3 criteria


Preliminary Forensic Protocol for Analysis of Single

Human Hair Fibres by FTIR-ATR Spectroscopy with

the aid of Chemometrics.

260

261

264

265

266

268

266

xxiv

LIST OF TABLES

Table 1.1: Amino acid Composition of Human Hair Fibres from

the Major Races (µmol/g)

12

Table 2.1: Specifications and Operating Parameters for the FTIR –

ATR Analysis

69

Table 2.2: List of Preference Functions 85

Table 3.1:

Table 4.1:

Table 4.2:

Table 4.3:

Table 4.4:

Table 4.5:

Table 4.6:

Table 4.7:

Table 4.8:

Table 4.9:

Major Vibrational Band Assignments of Human Hair

Keratin

Data matrix for ranking of Untreated, Mildly Treated

and Chemically Treated Hair Fibre Spectra by

PROMETHEE (3-Class Model)

PROMETHEE II Net Flows of the 1750 – 800 cm-1

Database


and Chemically Treated Hair Fibre Spectra (4-Class

Model)


Database (4 Class Model)

1690-1200 cm-1

Data matrix for ranking of Untreated,

Mildly Treated and Chemically Treated Hair Fibre

Spectra by PROMETHEE II


Database

1690-1500 cm-1

Data matrix required for ranking of

Untreated, Mildly Treated and Chemically Treated Hair

Fibre Spectra by PROMETHEE (3-Class)


Database

Summary of Chemometric Results for Current and

Alternative Spectral Regions of Raw and Second

Derivative Spectra

145

170

171

174

175

184

185

192

194

199

xxv

Table 5.1:

Table 5.2:

Table 5.3:

Table 5.4:

Table 5.5:

Table 5.6:

Table 5.7:

Table 5.8:

Table 5.9:

Table 5.10:

Table 5.11:

Table 5.12:

Table 5.13:

Table 5.14:

Table 5.15:

PROMETHEE Model for African-type Untreated,

Mildly Treated and Chemically Treated Hair Spectra

(1750-800 cm-1

)

PROMETHEE Model for ranking of African-type

Untreated, Mildly Treated and Chemically Treated Hair

Spectra (1690-1500 cm-1

)

PROMETHEE II Net φ Ranking of the African-type

1750-800 cm-1

Spectral Database

PROMETHEE II Net φ Ranking of the African-type

1690-1500 cm-1

Spectral Database

PROMETHEE II Model of the Entire Spectral Database

(257 spectra x 3PC Criteria) within the 1690-1500 cm-1

Spectral Region

PROMETHEE II Net φ Ranking of the 3 Race IR

Spectral Database 1690-1500 cm-1

PROMETHEE II Model of Untreated African Male

(NMUN 1) and Untreated Female Hair Spectra

PROMETHEE II Net φ Ranking of the Untreated

Spectral Database

PROMETHEE II Model of Male and Female Mildly

Treated Hair Spectra

PROMETHEE II Net φ Ranking of the Mildly Treated

Spectral Database

PROMETHEE II Model of Male and Female

Chemically Treated Hair Spectra

PROMETHEE II Net φ Ranking of the Chemically

Treated Spectral Database

PROMETHEE II Model of the Untreated Female

Spectral Database

PROMETHEE II Net φ Ranking of the Female

Untreated Hair Database

PROMETHEE II Model of the Chemically Treated

Female Spectral Database

213

214

215

216

222

224

231

233

238

239

244

245

251

253

256

xxvi

Table 5.16:

Table 5.17:

Table 5.18:

Table 5.19:

Table 5.20:

PROMETHEE II Net φ Ranking of the Female

Chemically Treated Hair Database

PROMETHEE II Model of the Mildly Treated Male

Spectral Database

PROMETHEE II Net φ Ranking of the Male Mildly

Treated Hair Database

PROMETHEE II Model of the Chemically Treated

Male Spectral Database

PROMETHEE II Net φ Ranking of the Male

Chemically Treated Hair Database

258

262

263

266

267

xxvii

ABBREVIATIONS

A

AFM

ATR

a.u.

C

CMM

Asian

Atomic Force Microscopy

Attenuated Total Reflectance

Arbitrary Units

Caucasian

Cell Membrane Matrix

cm-1

DAP

1/Wavelength

2-diamino-2,4-phenoxyethanol

DNA Deoxyribonucleic Acid

DRIFTS Diffuse Reflectance Infrared Fourier Transform Spectroscopy

ESEM

F

Environmental Scanning Electron Microscopy

Female

FC

FT-IR

Fuzzy Clustering

Fourier Transform Infrared

GAIA

GC/MS

GSR

Geometrical Analysis for Interactive Aid

Gas Chromatography/Mass Spectroscopy

Gun Shot Residue

HPLC High Performance Liquid Chromatography

IAEA International Atomic Energy Authority

IR Infrared

IRE Internal Reflection Element

IRS

Kb

M

MEA

MCDM

MT

Internal Reflection Spectroscopy

Kilo bases

Male

Methyleicosanoic acid

Multi-Criteria Decision Making (Techniques)

Mildly Treated

mt

N

nm

NMR

Mitochondrial

African-type

Nano-metres

Nuclear Magnetic Resonance

xxviii

No.

NTR

Number

African-type Treated

Nuc

(p)

PAP

PAS

Nuclear

Weighting Exponent for Fuzzy Clustering

Paraaminophenol

Photo-Acoustic Spectroscopy

PC Principal Component

PCA

PCR

PNG

PPD

PROMETHEE

% RH

RNA

SEM

Principal Component Analysis

Polymerase Chain Reaction

Papua New Guinea

Paraphenylenediamine

Preference Ranking Organisation Method for Enrichment

Evaluation

Relative Humidity (Percent)

Ribonucleic Acid

Scanning Electron Microscopy

SIMCA

SNR

STR

Soft Independent Modelling of Class Analogy

Signal to Noise Ratio

Short Tandem Repeats

TIR Total Internal Reflection

TR Treated

UN

UV/Vis

µm

α

Δ

δ

h

ν

Untreated

Ultra-Violet/Visible Light

Micrometers

Alpha, keratin proteins

Beta, pleated sheet proteins

Delta, Energy (kJmol-1

) or GAIA Δ %

delta

Planck‟s constant, 6.625 x 10-22

kJsec

Lambda, wavelength of electromagnetic wave (cm)

Nu, frequency of light Hertz (Hz)

1

1.0 INTRODUCTION

1.1 Prologue to the Investigation

Naturally occurring fibres such as human and animal hair are -keratin proteins.1 Such

fibres together with any plant, mineral, or synthetic fibres, are often found on victims of

crime, suspects or associated animals. They are frequently collected as trace physical

evidence in a wide variety of crimes for subsequent forensic analysis by crime scene

investigators.2-5

Fibre evidence such as hair which is associated with a crime scene is of

significant forensic value, because it can provide important information which may

assist in the investigation and prosecution of criminal cases.6-8

The detection or discovery of most classes of fibres at crime scenes are a regular

occurrence due to their ubiquity in nature.9 At any given time, we are constantly

surrounded by fibres in our daily lives, from the hairs that cover our body for protection

and insulation, to the textile fibres that comprise our clothing, furniture, vehicles and

floors.9-12

Furthermore, unless they are destroyed by fire, or degraded under strongly

acidic or alkaline conditions, the fibres maintain structural integrity for a longer period

of time than most other tissue types.13

14

This is due to the fact that they are

encapsulated by a fairly resistant external layer (i.e. the cuticle) which serves to protect

the fibre from adverse environmental conditions.10

Presence of trace evidence such as fibres at crime scenes is often the result of some

form of physical contact and exchange between the perpetrator and the victim and/or the

surroundings during the commission of a crime.13

15

This phenomenon of „exchange

evidence‟, is governed by a fundamental principle known as the „Locards Principle of

Exchange‟ which states that “every contact leaves a trace”.3 This principle is one of the

foundations of modern forensic science, and the detection of trace evidence is the

crucial key to the solution of crime.16

For human hair evidence, the current forensic methods of analysis rely on comparisons

of either hair morphology by microscopic examination or nuclear and mitochondrial

DNA analyses.17

Microscopic examinations of the morphological characteristics of

human hairs indicate the colour, thickness, shape, race, body area (e.g. scalp or pubic,

2

auxiliary (armpit, chest and limb regions)) and method of removal (e.g. naturally shed

or forcibly removed).5 17

Two problems have confronted researchers and examiners in

the forensic examination, comparison, and identification of human hair. First, the

ability among workers in different geographical areas have been frustrated owing to the

lack of an atlas that all workers could reference when describing a particular

characteristic or one of the hair variates.18

Second, the ability of the researcher to

develop frequency data for the variates of each characteristic have been hindered owing

to a lack of a uniform reference for identifying the specific microscopic characteristic

seen in a study hair.18

Complementary to microscopic analysis, nuclear and mitochondrial DNA analyses may

provide genetic profiles from an unknown source.17

19

DNA is unique to the individual,

and when compared, can form highly significant associations between known and

unknown hair samples.17

Unfortunately in some instances the utilisation of microscopy

and DNA analyses are difficult and often not feasible. For example in homicide and

sexual assault investigations, hair and synthetic fibres have often been influenced by

their immediate surroundings such as blood, grease and oil (i.e. hit and run cases),

smoke and fire, bodily fluids (e.g. seminal or vaginal fluid) or the broader environment

through burial, water immersion and wear.20

Hence, subsequent analysis and

comparison of such fibres is complex. Rendle affirms “In the absence of material

leading to recovery of DNA, the forensic scientist has to rely upon chemical analysis of

fibres in order to establish or eliminate links between suspect and victim and/or

scene”.21

In previous investigations22-27

, research has been dedicated to the study of the keratin

protein structure of single human hair fibres employing the structural elucidation

technique known as Fourier Transform Infrared (FT-IR) Spectroscopy. This approach

facilitates the characterisation of single hair fibres on a chemical/molecular level, and

thus has the potential to complement current forensic microscopic and genetic

examinations. However, in earlier or initial spectroscopic investigations, there were

restrictions or limitations to the quality of the spectra obtained by the specific technique

(FTIR-Microspectroscopy – Transmittance), and also because the sampling populations

were small.

3

In more recent studies it has been discovered that FTIR-Attenuated Total Reflectance

Spectroscopy (FTIR-ATR) produces spectra of high quality, avoiding high absorbance

of IR radiation and eliminating saturation or “peak saturation”.23

Sample preparation is

easier and although the technique requires a small area of the fibre to be compressed, it

is relatively less destructive, when compared to the rolling technique that had been

utilised by previous investigations which is known to change the conformation of the

protein.23

Finally, FTIR-ATR spectroscopy is economical on time.

To compare and discriminate the minute differences between spectra from different

individuals with varied levels of cosmetic chemical treatment (i.e. from no treatment to

bleached and dyed), Panayiotou,24

Paris25

, Barton23

, McCarthy26

and Brandes27

(NIR

spectroscopy), analysed and interpreted the results with the aid of Chemometrics.

Chemometrics is primarily concerned with the extraction of significant information

from large data sets.28

29

From the various multivariate data analysis techniques that

exist to solve chemical problems, exploratory Principal Component Analysis (PCA),

Classification techniques such as Fuzzy Clustering (FC) and Soft Independent

Modelling of Class Analogy (SIMCA), and Multi-criteria Decision Making (MCDM)

techniques were amongst those most heavily used to aid spectral analyses.

As a result of several investigations, at this stage a single human hair fibre can be

discriminated from other human hair fibres on the basis of cosmetic chemical treatment,

gender and race using a small to medium population size, focusing on of shaft (i.e.

middle to root section) spectra only.23

However, these separations have not yet been

fully justified, for example the discrimination of male and female hair fibres and the

relationship between untreated and treated African-type fibres.

Hence, a further insight into the structural chemistry is necessary as it provides

information of hair from all human races.

The global perspective of continuous research and development into this specific field

of science seeks to provide forensic authorities with a rapid methodology for

discrimination of single unknown human hair fibres via FTIR-ATR Spectroscopy

coupled with Chemometrics. The procedure should offer critical evidence or

information pertaining to the chemical nature of the fibre including the cosmetic

4

treatment, gender, and major race of the suspect/perpetrator from only a single human

hair fibre. It is envisaged that such a study will provide a comprehensive database of IR

spectra of fibres originating from individuals of different race and also different

cosmetic chemical treatments.

Thus, within the scope of this project, the principal aim involves investigating the

provisional, unverified protocol suggested by Panayiotou.24

This will be achieved

by detailed examination of the FTIR-ATR spectra of single hair fibres with the aid

of novel approaches in this topic such as:

a) Spectral subtraction to determine the key spectral differences between

various types of fibre i.e. gender and race (Chapter 3).

b) Derivative spectroscopy i.e. second derivative spectra to unravel the

complexity of the keratin spectra and illustrate the underlying principles

for the separations (Chapter 3). The objective here is to gain an

understanding of any spectral differences based on the above classifications

and to assist the information gained from (a) (Chapter 3).

c) On the basis of (a) and (b), a novel investigation of potential classification

of hair spectra with the aid of various chemometrics methods such as Fuzzy

Clustering (FC), PROMETHEE and GAIA over alternate wavenumber

ranges (i.e. between 1750-800 cm-1

) selected on the basis of the detailed

studies in Parts (a) and (b) (Chapter 4).

The development of a protocol based on the conditions above has the potential to

facilitate the discrimination of male and female hair fibres, as well the more

complex separation of untreated and chemically treated African-type hair fibres.

From the forensic perspective, this information will significantly narrow

down the population of potential suspects to a given race (Chapter 5).

5

Investigation of chemically treated hair fibres is also warranted using the

proposed protocol. Such fibres are arguably more common in our society

than the untreated ones. This work will add an important dimension to the

protocol which has only been addressed briefly by the previous

investigations as at this stage the majority of the work was concerned with

non-treated hair fibres only.

In addition to the above aim is to explore the possibility of sub-dividing

treated hair fibres into different classes as previous studies suggest

ambiguity between an untreated/virgin hair and a physical-chemical

treated hair (Chapter 4 and 5).

Multi-criteria decision making (MCDM) techniques such as PROMETHEE

ranking supported by the GAIA interpretation of these results, has been

shown to be useful in a number of studies in which the relative ranking

order provided an alternative method for classification of objects and their

comparison to selected references.24

This methodology will be applied for

comparison of single hair fibres (Chapter 4 and 5).

The remainder of this chapter focuses on the morphology, chemical structure and

physical properties of human hair keratin. Attention is especially given to the cosmetic

chemical treatments that are applied to hair fibres for personal and social purposes, as

well as the mechanical processes that can also have an effect on the hair structure. The

significance of forensic hair fibre evidence to a criminal investigation is also discussed.

The chapter will conclude by incorporating an essential examination of the current

methods employed to characterise hair fibres, highlighting the need to introduce and

explore other complementary instrumental techniques such as FT-IR spectroscopy.

The second chapter is concerned with the samples, instrumentation, procedure and

statistical software used to analyse the spectra for the investigation. The remainder of

this chapter focuses on the theory and applications of Chemometrics and Multi-Criteria

Decision Making techniques.

6

The third chapter focuses on the critical examination and comparison of the structural

chemistry of keratin and its corresponding FTIR-ATR spectra. The spectra were

collected from fibres from a broad number of individuals of both genders encompassing

the major human races (i.e. Caucasian, Asian, and African-type). The similarities and

differences of raw, subtracted spectra and second derivative spectra of the above types

of fibre are discussed. To support the conclusions of the spectral examinations,

morphological analysis of the cuticle surface topography of the various fibre types will

be conducted through SEM.

The fourth chapter deals with the continued development and inspection of the current

proposed forensic protocol24

for analysing human hair fibres through FTIR-ATR

spectroscopy aided by Chemometrics. This was achieved through an investigation of

various spectral regions to match and discriminate single hair fibres.

The fifth chapter is concerned with the robustness and applications of the optimised

protocol (i.e. Chapter four) to investigate specific scenarios such as the analysis of

African-type hair fibres; the structural differences of spectra between male and female

fibres; the structural differences between races; the separation of single/multiple treated

hair fibres.

The sixth chapter summarises the key findings of the investigation in relation to the

aims and objectives (Section 1.6) and concludes by suggesting ideas for further or

future studies in this field.

1.2 Human Hair Fibres

Hair (the stratified epithelium) is an appendage of the skin that proliferates from large

cavities or sacs called follicles.11 12

The length of the hair extends from its root or bulb

embedded in the follicle, through the dermis, epidermis, stratum corneum, skin, then

continues into a shaft and terminates at the tip end.11

Hair fibres constitute the characteristic outer-covering of all mammalian skin and serve

a number of specific purposes, principally protection.11

30

31

Human scalp hair creates a

physical barrier from the immediate surroundings, protecting the surface of the scalp

7

and the body respectively during exposure to a wide range of harsh environmental

conditions.10

30

31

1.2.1 The Morphology of Human Hair Fibres

Morphologically, three distinct varieties of cells or units are produced in the follicle

which ultimately results in the formation of the three basic structural layers of any

human hair fibre.10 11

The three layers are: the external Cuticle layer, the Cortex, and

the Medulla (not illustrated). A schematic diagram of a typical human hair fibre is

presented in Figure 1.1.

1.2.1.1 The Cuticle

The outermost or external layer of the fibre consists of flattened overlapping scales

known as the cuticle (Figure 1.1), which is responsible for much of the resistance and

stability of the hair.10-12

Figure 1.1 – A schematic diagram of a human hair fibre illustrating the morphological

features starting from the external Cuticle, Cortex, Macrofibril, Microfibril down to the

-Helical Protein. (Hand Illustrated and Adapted from10-12

31

).

Cuticle

Layers

Cortex

Microfibril

Macrofibril

-Helical Protein

8

In developed hair, the cuticle cells are square sheets approximately 0.5 µm-1.0 µm thick

and 50 µm in length, with an overall thickness of approximately 5-10 scales.11 32

The

proximal ends are strongly attached to the cortex whilst the distal free edges protrude

toward the tip end of the fibre. As a consequence of the extensive overlapping (which is

approximately 80 % of their length), the cells slightly tilt away from the fibre axis

giving the hair surface a “tiled roof” appearance which in turn allows follicular

anchorage of the growing hair. The architecture of the surface also facilitates the

removal of trapped or adhered dirt particles and detached cuticle cells.32

A schematic cross-section of a developed cuticle cell is illustrated in Figure 1.2. Each

cuticle cell is enclosed and separated by a strongly adhesive layer known as the cell

membrane matrix (CMM). The CMM is made up of a central, polysaccharidic δ-layer

enclosed by two lipid-rich β-layers. An important lipid constituent of the CMM is 18-

methyleicosanoic acid (18-MEA), which is covalently connected to its protein

components. It has also been established that a thin layer of 18-MEA is grafted onto the

outer surface of each cuticle (upper layer).33

The lipid film attributes to the surface

having low friction with concomitant hydrophobic character.

Figure 1.2 – An illustration of the cross-section of a developed cuticle cell. (Adapted

from10 11 31 32

)

A-layer (Cystine rich)

δ-layer

Epicuticle

Outer β-layer

Fibre Surface Outer β-layer

Epicuticle

Exocuticle

Endocuticle (Cystine-

deficient)

Inner β-layer

Inner layer

9

The mature cuticle cell is comprised of a number of distinct layers namely the

epicuticle, A-layer, exocuticle and endocuticle which have different levels of proteins,

lipids and carbohydrates.

The epicuticle is a thin membrane that is a by-product of the reactive modification of

other sub-components of the cuticle.34

AFM studies have illustrated that the epicuticle

is a continuous layer 13 nm thick, covering the entire outwardly facing intracellular

surface of every cuticle cell.35

The epicuticle is approximately 80 % protein and about

5 % lipid with no evidence of carbohydrate.36

It is a membrane which is an integral

part of the individual cuticle cells and is chemically resistant.

The A-layer is cystine-rich (30%), and is characterised as a biochemically stable layer,

which strongly resists physical and chemical forces.11

This layer adjoins the major

component of the cuticle, the exocuticle, which represents two-thirds of the cuticle

structure. The proteins of the exocuticle are densely cross-linked by disulphide bonds

of cystine (15% cystine-rich), but not as extensively as the proteins of the A-layer. The

next adjacent layer is the endocuticle and is cystine-deficient (~ 3%), containing much

of the non-keratinous cellular debris and a high content of basic and acidic proteins.32

1.2.1.2 The Cortex

Surrounded within the protective layer of the cuticle is the cortex which constitutes the

central core and the main bulk (90 % by weight) of the hair shaft.10

11

30

31,37

The cortex

is largely responsible for the mechanical properties of the fibre and is composed of

elongated, spindle-shaped cortical cells packed tightly together which are oriented

parallel to the axis of the fibre.11

10

30

31

The cortical cells are approximately 100 µm long and 5 µm across at the maximum

width aligned along the axis of the fibre.37

Each cell is made up of fine microfibrils

which are furthermore comprised of -helical proteins. Microfibrils are approximately

7 nm in diameter and are grouped into larger bundles of rods called Macrofibrils (≈100-

400 nm in diameter) which represent up to 60% of the cortex material by mass.32

These

macrofibrils are embedded in an amorphous protein matrix.37

10

The macrofibrils exhibit different variations in packing dispositions within the cortex,

and have been designated as paracortex and orthocortex. They are readily discerned in

fibre cross-sections in TEM images. The ortho- and para-cortices are approximately

hemi-cylinders wound round each other helically in phase with the crimp of the fibre so

that the paracortex is always placed on the inside and the orthocortex on the outside of

the crimp curvature.36

1.2.1.3 The Medulla

The inner structure of the hair fibre (not illustrated in Figure 1.1), with a diameter of

about 5-10 µm is the medulla.37

This layer essentially represents a group of specialised

cells which are vacuolated and are aligned either continuously or discontinuously along

the central axis of the fibre. The medulla may also be either completely absent or in

some instances a double medulla may be observed.11

The medulla has high lipid content compared to the rest of the fibre which is deficient in

cystine however its rich in citrulline.38

39

Morphologically, the medulla has a porous

structure formed by sponge-like keratin and some vacuoles filled with air resulting from

the differentiation process.40

41

A layer of CMM separates the medulla from the

cortex.42

1.2.1.4 Melanin Pigment and Greying of Hair

Another important component of human hair is melanin. This refers to the pigment

granules (≈200-800 nm in size) that impart the characteristic natural colours to the

fibre.37

Melanin forms dense ovoid or rod-shaped granules and these are of two basic

colour varieties interspersed throughout the medulla, cortex and in greater concentration

towards the peripheral portion of the cortex.11

10

30

The two types of melanocytes are

eumelanin, which produces the dark shades such as brown and black; and pheomelanin,

which is responsible for the lighter colours such as red and yellow.31

Both melanocytes

originate from the oxidation of the amino acid tyrosine with the aid of the enzyme

tyrosinase.11,30,31

The proposed mechanism involves the oxidation of tyrosine to

dopaquinone, then depending on the amount of cysteine present, it forms indole

intermediates then eumelanins, or 5-S-cysteinyldopa and pheomelanins.11

11

However, when the melanin granule cells cease to produce pigment, the hair fibre turns

grey and white. Hair greying is a natural age-associated feature in humans. While the

normal incidence of hair greying is 34 ± 9.6 years in Caucasians and 43.9 ± 10.3 in

Africans43

, on average 50 % of people have at least 50 % ± 5 grey hair at age 50, in a

cohort of Caucasians.43

This is irrespective of sex and initial hair colour. Global

greying of the scalp has been described as a gradual and progressive process occurring

over more than 15 years in humans.43

The cellular and molecular origins of greying are

poorly understood, however, the decrease in melanin synthesis appears to be associated

with a decrease in tyrosinase activity.43

1.2.2 The Chemical Structure of Human Hair Fibres

1.2.2.1 α- Keratin Proteins

Human hair and all other mammalian hair fibres belong to a group of fibrous proteins

known as -keratin.1 10 30

44

The keratin family of fibrous proteins are found in the

higher vertebrates (reptiles, birds, and mammals). Keratins are the principal

constituents of ectodermal tissues such as hair, wool, furs and epidermis. They also

make up a majority of the appendages derived from the skin, which includes nails,

claws, scales, hooves and feathers.30 36

44

45

Keratin constitutes roughly 85% of the

mass of a single fibre and contributes to a range of essential functions which include

physical and chemical protection against the influences of the environment (e.g.

temperature control, rain, ultra-violet radiation emitted from the sun, etc.) and also

provides mechanical strength to the fibre.36

Keratin is a high molecular weight polymer

containing polypeptide chains formed by the condensation of L-amino acids as shown

by Figure 1.3:

NH2

R

HOOC R

NH2

HOOC

NH

R

HOOC

R

NH2

O

OH2

+-

Figure 1.3 – The condensation reaction of amino acids.44

1

2

1

2

12

The bond that forms upon condensation which links the amino acids is called the

peptide bond.36

A number of these condensation reactions will ultimately produce a

polypeptide chain. The polypeptide chain becomes the backbone of the -keratin fibre.

The R1 and R2 group signifies the side chains of the amino acid residues for -keratin

corresponding to 18 different compositions from the major races (Table 1.1).

Table 1.1 – Amino acid Composition of Human Hair Fibres from the Major Races

(µmol/g)32

Amino Acid African Brown Caucasian Asian

Alanine 370-509 345-475 370-415

Arginine 482-540 466-534 492-510

Aspartic acid 436-452 407-455 456-500

Cysteic acid 10-30 22-58 35-41

Glutamic acid 915-1017 868-1063 1026-1082

Glycine 467-542 450-544 454-498

Histidine 60-85 56-70 57-63

Isoleucine 224-282 188-255 205-244

Leucine 484-573 442-558 515-546

Lysine 198-236 178-220 182-196

Methionine 6-42 8-54 21-37

Phenylalanine 139-181 124-150 129-143

Proline 642-697 588-753 615-683

Serine 672-1130 851-1076 986-1101

Threonine 580-618 542-654 568-593

Tyrosine 179-202 126-194 131-170

Valine 442-573 405-542 421-493

½ Cystine 1310-1420 1268-1608 1175-1357

13

Besides serine and glycine, hair fibres exhibit the presence of a large concentration of

the sulphur-containing diamino acid cystine that largely contributes to the stability of

the fibre (Figure 1.4).11

30

31

NH3

+ SS

NH3

+

OO

O O

Figure 1.4 – Molecular structure of the amino acid Cystine.

1.2.2.2 Bonding Mechanisms in Keratin – Covalent and Non-covalent Forces

In the -keratin arrangement, cohesion or structural stability of the hair fibre is provided

by a variety of bonding mechanisms. These range from networks of covalent cystine

cross-linkages to weaker secondary interactions such as coulombic interactions between

side chain groups, hydrogen bonds between neighbouring groups, van der Waals

interactions and, in the presence of water, hydrophobic bonds.10 30

31

The covalent cystine linkages or disulphide (-S-S-) cross-links are the strongest type of

bonds or associations present, and contributes significantly to the physical and chemical

properties of hair keratin.10 30

45

46

The disulphide linkages in hair keratin are the result

of an oxidation reaction between adjacent thiol (-S-H) groups of opposing cysteine

molecules in the polypeptide chain, consequently forming a molecule of cystine.31

45

Coulombic interactions, occasionally referred to as salt links, are electrostatic forces

acting between ionised acidic and basic side chain residues, i.e. the negatively charged

carboxylic acid groups (-COO-) and positively charged amino groups (NH3

+).

10 30 31

14

Two types of hydrogen bonding exist in the -keratin structure.10

One type is present

between water molecules and hydroxyl groups (-O…H-O-) and the second type is

between the amide and the carbonyl group (Figure 1.5) and the amide C=O of side

chains.

N H O........

Figure 1.5 – Hydrogen bonding between the amide and carbonyl groups in the -

keratin structure.

Van der Waals interactions play a non-specific role in the cohesive binding of the

chains and side chains of -keratin fibres. Finally, hydrophobic bonds (only in the

presence of water) have a specialised task of binding the single -helices into double -

helical ropes which form intermediate filaments.10

Cosmetic chemical treatment processes such as bleaching, permanent dyeing,

permanent waving, straightening, photo-oxidative bleaching (sun exposure) and

chlorine oxidation (through swimming), all affect the structural chemistry of the -

keratin fibre. These processes target the bonds that provide stability to the fibre. From

a forensic perspective, a fibre that has been chemically altered can be of great

importance as it can be discriminated from untreated fibres.47

1.2.3 The Chemical Process of Bleaching Human Hair Fibres

The primary objective of cosmetic bleaching is to lighten the natural colour of hair and

this is most readily accomplished by oxidation.31

48

This is achieved through partial or

total decolourisation of the hair‟s natural melanin pigment by the reaction with an

oxidising agent.31

48

Hair bleaching formulations consist of solutions of up to 12%

hydrogen peroxide and ammonia to give a final pH around 10, and thickeners. If

extensive bleaching is required a “bleach booster” (usually ammonium and potassium

persulphates) are added to the peroxide.

15

1.2.3.1 The Mechanism of Bleaching

The bleaching process follows two steps. Firstly a fast dissolution step occurs in which

the pigment granules disperse and dissolve. This is then followed by a much slower,

decolouration step.31

As a consequence of the decolourisation of the melanin, a

secondary effect also takes place whereby side reactions alter the properties of hair

keratin producing oxidative or “bleaching” damage. The damage is caused by the

oxidative cleavage of the disulphide bonds or cross-links to form cysteic acid.31

48

Severe bleaching also reduces the concentration of free sulphydryl groups and to a small

degree degrades other amino acid residues such as tyrosine, threonine, and

methionine.31

48 49

As a result, the fibre structure is weakened with a lower cross-link

density and overall its hydrophilic nature is increased, due to anionic site formation e.g.

cysteic acid residues.31

48

In particular the fibre feels more brittle, is more susceptible

to breakage, becomes more porous and hence will absorb larger amounts of water. 31

1.2.3.2 The Disulphide (S-S) Cleavage Mechanism

The mechanism for the oxidative cleavage of the disulphide bond during the chemical

bleaching of human hair is predominantly through an S-S cleavage process (Figure

1.6).50

It is understood from this mechanism that oxidation of cystine principally

produces cysteic or sulphonic acid (-SO3H). Accompanying this, there is also the

formation of oxidative intermediates such as the cystine monoxide (-SO-S-) and cystine

dioxide (-SO2-S-).

RS

SR

RS

SR

O

RS

SR

O

O

R SO3H

Figure 1.6 – Scheme of the S-S cleavage mechanism for the bleaching process.

Evidence of oxidative cleavage is provided with the use of characterisation tools such as

infrared (IR) spectroscopy. IR studies have shown that absorbance bands at 1044 cm-1

,

1071 cm-1

and 1121 cm-1

can be correlated to the characteristic stretches of the cysteic

acid, cystine monoxide and cystine dioxide bonds respectively.47

51-53

16

1.2.4 Chemical Process of Hair Dyeing and Colouring

Hair colouring can be indexed and classified into three major categories: temporary

(surface dyeing), semi-permanent and permanent (oxidative) hair dyeing.31

48 54

55

1.2.4.1 Temporary Colourants

Temporary colourants are used for single events only and are readily removed by

shampooing and to a lesser extent by rinsing with water.31 54

55

Colouration occurs by deposition of acid dyes on the surface of the hair. The dyes

contain cationic surfactants or cationic polymers to allow the dye to complex to the

anionic surface.31

1.2.4.2 Semi-Permanent Colourants

This class of dye will remain for four to six weeks before needing reapplication. 31 54

55

Major uses have been for grey coverage or blending, highlights or brightening of one‟s

own hair colour.31

The mechanism does not involve covalent bonding rather it relies on

the diffusion of the coloured molecules from solution into the hair cortex. The product

contains a number of dyes blended to give the desired shade. The dyes are dissolved or

dispersed into a detergent base. As the dyes differ in molecular size, the tip end of the

hair fibre retains larger molecules and smaller molecules are retained by the root end

but diffuse freely in and out of the tip end.

Typical dye components comprise:

• yellow and orange ortho- and para-nitroanilines and nitrodiphenylamines,

• yellow to violet nitrophenyldiamines and nitroaminophenolic ethers

• violet to blue amino and hydroxyanthraquinones.

Semi-permanent dyes are also formulated with solvents, surfactants, foam

stabilisers/thickeners, and an alkalising agent.

17

1.2.4.3 Permanent or Oxidative Dyeing

Permanent hair colouring involves the migration of colourless/light coloured and low

molecular weight precursors (a base dye intermediate and a coupler) into hair with

subsequent oxidation with hydrogen peroxide and concurrent bleaching of the natural

melanin pigment by one or two shades. The oxidative polymerisation of monomer dyes

results in the in-fibre formation of indo-dyes, thus imparting colour to the hair fibre. 31 55

56

57 Therefore commercial oxidative hair dyes consist of three major components for

the dyeing process:

• Primary intermediates such as amino (e.g. Paraphenylenediamine (PPD)) and

hydroxy (paraaminophenol (PAP)) aromatic compounds that form colour upon

oxidation.

• Couplers (modifiers), which react with the products from oxidation of the

primary intermediates to form dyes (e.g. Phenols, 2-diamino-2,4-phenoxyethanol

(DAP), and meta-diaminobenzenes).

• An Oxidant, which is commonly hydrogen peroxide, although urea peroxide and

peroxide generators such as perborate have been used.

Other components include an alkaliser (e.g. ammonia), surfactants (oleic acid

derivatives or non-ionic ethoxylated phenols), antioxidants (sodium sulphite) and metal

chelating agents (ethylenediaminetetracetic acid).

1.2.5 Permanent Waving and Straightening of Human Hair Fibres

Chemical or permanent waving and also straightening are two important hair-care

treatments that involve association of almost every aspect of hair structure manipulation

to accomplish their objectives.32

Both processes endeavour to construct a durable

configuration that is different from what an individual‟s hair exhibits in its native

form.32

Wolfram states “The hair has a geometry that is the result of the processes of

keratinisation and follicular extrusion, transforming a viscous mixture of proteins into

strong, resilient, and rigid fibre”.32

Essentially, waving and straightening can be

perceived as a combination of reversal and stepwise restaging of these processes,

18

involving the softening of the keratin and molding and annealing the newly conferred

hair geometry.

1.2.5.1 Chemical Process of Permanent Waving

Permanent hair waving is regarded as a complex proces.31 58

The waving of hair is

accomplished by the fission or the reduction of the disulphide bonds by mercaptans such

as thioglycolic acid (Figure 1.7)11

:

KS

SK R-SH R-S-S-R K-SH+ 2 + 2

Figure 1.7 – Reaction scheme between the disulphide bond and a mercaptan where K

represents the Keratin chain and R represents the amino R-group side chains.

Different types of perms are available however the chemical principle is similar in all

perming solutions and the key steps are summarised as follows58

:

1. The hair is initially washed and then placed on curlers dependent on the degree

of curl desired.

2. After setting the hair, alkaline agents such as ammonia and ammonium

hydroxide (pH 9), are applied to the hair to lift the scales of the cuticle so as to

allow the perming solution to reach the cortex.

3. The reducing agent (thioglgycolates or bisulphites) cleaves some of the

disulphide bonds in an equilibrium process as depicted in Figure 1.6. The thiol

groups can be easily oxidised by atmospheric oxygen, and thus the stabilisation

of the reduced species involves blocking the thiol group with iodoacetic acid or

cross-linking with dihalogenoalkanes (e.g. dibromomethane).

4. With the bonds broken, a molecular rearrangement can take place where new

bonds will be created according to the new shape of the hair.

19

5. The disulphide cross-links are reformed using an oxidising agent such as sodium

bromate, hydrogen peroxide.48

The cuticle scales return to their original

position.

1.2.6 Hair Straightening

Hair straightening formulations designed for most African-type hair employ strong

bases such as sodium hydroxide as the active ingredient. The process involves fission

of the disulphide bond by hydrolysis or nucleophilic substitution of sulphur by the

hydroxide ion. Straightening can also cause damage to the stable peptide bond. SEM

studies on relaxed hair revealed that the cuticle cells are removed causing extensive

damage to the cortex.59

The decreased cross-link density leads to increased swelling,

which makes the fibre more susceptible to surface damage during normal handling

procedures.48

1.2.7 Photo-oxidative Bleaching

Prolonged exposure of keratin to sunlight which contains UV irradiation leads to

destructive changes in the keratin structure.48

The primary reaction in the weathering of

human hair involves the oxidative cleavage of the disulphide bond in keratin to cysteic

acid.11

52

Exposure to sunlight can also lead to bleaching of the melanin pigments as

well as degradation of the keratin fibre.60

The mechanism for photo-oxidative bleaching

follows a C-S fission route (Figure 1.8)11

:

Figure 1.8 – C-S fission (E = hν) mechanism of -keratin by photo-oxidative bleaching.

RS

SR

RS

SOH

RS

SO2H R

SSO

3H

R-SO3H

H2SO

4

R-OH

+

+

h

20

1.2.8 Oxidation of Hair with Chlorine

When hair is treated with chlorine water, bubbles or sacs form at the surface of the

fibre.11

Depending on the pH of the water, the oxidising species Cl2 or HOCl cleave the

disulphide bond and the peptide bond. The bubbles diffuse across the cuticle producing

smaller, water soluble species too large to migrate out of the hair. As a result, the fibre

swells.11

Studies concerning the effects of chlorine in swimming pools on hair,

concluded that it increased fibre friction on the surface.61

The composition of the keratin fibre therefore, has an influence upon its reaction to

various chemicals. Its physical structure has an influence upon its mechanical

properties.

1.2.9 Physical Properties of the α-Keratin Fibre

The physical properties of hair include mechanical properties (i.e. tensile properties,

strength and elasticity), thermal, electrical, frictional, adsorption and behaviour with

water (i.e. the keratin-water system).10 11 31

1.2.9.1 Mechanical Properties of the Keratin Fibre

The physical properties of human hair fibres are dependent on moisture content and

temperature. Under conditions of low temperature or short times for which no

structural mobility can occur in an -keratin fibre, the mechanical properties of the fibre

will depend primarily on the whole cohesive bond network.10

In the presence of water,

certain cohesive bonds permit the structure to flow. However, other components are

unaffected by water. Speakman62

demonstrated that the longitudinal stress-strain

relationship for a fibre equilibrated at a fixed relative humidity and at a fixed

temperature could be represented by three distinct regions of extension, known as the

stress-strain curve. When the fibre is initially extended, the fibre has a near linear

stress-strain relationship (up to a few percent extension). At approximately 0.2 %

strain, the crimps are removed from the fibre by unbending. Beyond 1 % strain, the

relationship is linear and is referred to as the Hookean region. An extension up to 25-30

% results in a small increase in stress to the fibre and is termed the Yield region.

21

Further extension beyond the yield region increases the strain and the fibre stiffens and

eventually breaks (i.e. the post-yield region).

1.2.9.2 The Keratin-Water System

Water is an important variable component of keratin fibres. At fixed temperatures, the

relationship between the equilibrium moisture content (% water regain) and the %

relative humidity of -keratin fibres shows a sigmoidal hysterisis curve.10

Water enters the fibre keratin structure via a diffusion process.10

As water is a highly

polar molecule, it interacts with the hydrogen bonds and other polar groups in the -

keratin chains.10

Amino acid residues with hydrophilic side chains lead to water

attachment equivalent to that of water hydration in a salt at low humidities. At higher

humidities, water enters the fibre as „solution water‟ not attached to specific sites but

with absorption resulting from the free energy difference arising from the entropy of

mixing keratin with water. Nuclear magnetic resonance (NMR), has facilitated the

determination of the amount and nature of the water in the keratin-water system.10

The

results suggest that the system consists of an interpenetrating polymer network made up

of a continuous hydrogen bonded water system with the matrix protein as well as with

the microfibril protein.

Experimental data concerning moisture binding by hair of different racial background

illustrates that no significant differences exist in the water uptake, regardless of the

relative humidity.32

Results are also available on the effect of cosmetic chemical treatment on moisture

uptake by hair fibres. At ambient humidities (65% RH), there is negligible water

absorption compared to the untreated or intact hair fibre. However, drastic increases in

fibre swelling or liquid retention can be observed upon wetting.32

22

1.2.10 The Effects of Mechanical or Physical Processes on Human Hair Fibres

In conjunction with our day-to-day habits, human hair is under regular abrasion or

weathering associated with hair grooming. SEM studies by Swift and Brown63

, Garcia

et al.64

and Robinson65

have shown that normal hair care treatments such as brushing,

combing, shampooing, towel drying and weathering by exposure to rain, sunlight (UV

radiation) and dirt all result in physical damage to the surface of the fibre.

1.2.10.1 Effects of Shampooing, Conditioning, Combing, Grooming and Towel Drying

Shampoo is used to clean hair and conditioner is used to coat the hair with a thin film in

order to protect it.66

Shampoo and conditioner can keep hair smooth, strong and easier

to comb.66

Friction is experienced when combing as a result of interactions between

hair and the comb material and needs to be low in order to facilitate the maintenance

and sculpting of the hair.66

To minimise entanglement, adhesive force needs to be low.

For complex and curly hair styles, higher adhesion between fibres is needed.66

This is

known as the hairs‟ Tribological (surface roughness, friction, adhesion) properties.66

Experiments have been performed to mimic the actions of shampooing and towel

drying. It was concluded that sections of the fibre closest to the root exhibited scales

with free edges of relatively smooth contour. However, at increasing distance from the

scalp, the scales became more damaged with jagged-like edges, causing them to be

lifted away and ultimately completely removed.

Conditioner consists of cationic surfactants, fatty alcohols, silicones and water which

thinly coat the hair, primarily through Van der Waals attractions.66

Beard et al.67

showed that conditioner treated hair fibres resulted in dramatic changes to the surface

composition with increasing amounts of silicon due to the dimenthicone in the

formulation and long chain fatty acid esters in the di-ester quat molecules.

Atomic Force Microscopy (AFM) topography studies66 68-71

have demonstrated variation

in the cuticle structure with cracking and miscellaneous damage occurs at the cuticle

edges in virgin hair. It was suggested that this damage was caused by mechanical

abrasions resulting from daily activities such as washing, drying and combing.

23

Frictional forces are seen to be higher on damaged hair than on virgin hair, due to the

increased roughness and a change in surface properties resulting from exposure to

chemical damage.

1.2.10.2 Effects of Thermal Treatments on Human Hair Fibres

In hair styling and grooming, temporary curling is often achieved with the application of

heat from a curling iron. Ruetsch et al.72

carried out an SEM investigation on untreated

hair to investigate the damage caused to the cuticular structure with the use of a curling

iron. Short and long-term curling of dry and wet hair were considered.

Dry hair fibres, were minimally damaged with the use of the curling iron for short

periods (10 seconds) and with normal applied tension; on the other hand, prolonged

contact times (10 minutes) combined with increased tension (10-30 g) lead to

compression, disintegration, radial cracking, and scale edge fusion of the surface cuticle

cell.72

With wet hair fibres, repeated short-term curling resulted in less damage to the cuticle

than with short-term use on dry hair. However, repeated long-term use led to the

distortion of the cuticle cell an effect which was attributed to trapped moisture

expanding in the form of steam, creating bulges in the scale faces and ripples at the

scale edges in the fibre.72

Ten minute contact under increased tension produced damage

which was significantly different from that observed with the dry hair fibre. In addition

to compression, disintegration, radial cuticular cracking, and scale edge fusion, fine-line

cracking was observed to be scalloped around the fused scale edges.72

The high

temperature flow of water-plasticised cell proteins created mutilated and distorted

cuticle cells.

In regards to the change in mechanical properties of the hair fibre, SEM images

illustrated that repeated short-term curling-cooling increased the post-yield modulus of

the hair fibres, possibly due to thermally induced cross-linking of components of the

cortical domains.72

Finally, in relation to the fibre‟s fatigue resistance, the results

showed that if the fibre was conditioned, the fatigue resistance increased. It was

suggested that this was a result of the conditioning compounds enhancing the heat-

24

induced cross-linking in the form of salt linkages and hydrophobic bonds, which led to

significant increases in fatigue resistance.72

Therefore, potentially, a human hair fibre can be recovered from a crime scene that has

undergone various chemical treatments or physical-mechanical processes. From a

forensic perspective, understanding how the processes operate allows the investigator to

have a greater appreciation to the grooming habits or routines of the suspect/victim.

Hence having covered the various treatments, it is important to focus on hair in the

context of forensic fibre evidence, which is the principal purpose of this investigation.

1.3 Forensic Science: Trace Physical Evidence

Forensic, from the Latin word forensis (forum) as “of or used in courts of law”.73

Forensic science refers to “the application of matters of law”74

with specialised fields

which includes the analysis of trace evidence. Trace evidence may be defined as

physical evidence of minute size in the form of human hairs, textile fibres, soil, glass

and paint fragments, arson accelerants, explosive residues, blood, bullet fragments,

fingerprints, plant debris, cosmetics and numerous other forms that require microscopic

comparison.20

75

76

Prior to the advent of DNA profiling, these materials constituted the

main types of supposed trace evidence.21

Trace physical evidence is readily exchanged

between the crime scene, the victim and the perpetrator of the crime.13

20

In the absence

of DNA evidence and fingerprints, trace evidence of this nature may be the only means

of solving of a crime.21

DeForest states “trace evidence has an important role to play in

both the investigative and adjudicative phases of a case”.77

For forensic scientists,

detectives and prosecutors, the presence, detection and recovery of trace evidence is

crucial and highly significant to an investigation.

DeForest et al. state “The use of trace evidence in criminal investigations and

subsequent prosecutions depends on its recognition and preservation at the scene of the

crime and its identification and comparison with exemplars in the forensic science

laboratory”.78

Therefore in the investigation of crime, hair or textile fibres from

questioned or unknown origins that are located on the victim and/or the immediate

surroundings are taken as corroborating evidence to link a suspect to a crime scene.4

When properly examined and interpreted, a common origin or connection between

25

evidential and known hairs can be established. This enables a suspect to be connected

to a crime, or alternatively, exonerated from a crime.4 79-82

However depending on the

nature or condition of the fibre, the association may have great probative value, or very

little, or even none at all.82

How trace evidence arises during the act of a crime is very simple. It is governed by the

fundamental principle or theory in forensic science, known as “Locards Principle of

Exchange” or the “Exchange Principle” formulated in 1910 by the French criminologist

Edmund Locard.75 83

82 84

He postulated that “every contact leaves a trace”, essentially

meaning that during the commission of a crime involving some form of physical contact

between two bodies or surfaces, a cross-transfer of evidence results.83

75 82

Small

amounts of materials from each object are transferred to their opposing surface. Locard

maintained that “the criminologist re-creates the criminal from traces left behind, just as

an archaeologist reconstructs prehistoric beings from his finds”.85

Prime examples of this phenomenon can be witnessed with fingerprints on various

surfaces, shoe sole impressions in soil, and more importantly with the transfer of fibres

both hair and textile between individuals and the surrounding environment during a

crime.2 75

80

82

1.3.1 Forensic Fibre Evidence

Fibre evidence is an important asset, which can provide valuable evidence in the

investigation and prosecution of criminal cases.6

86

87 Fibres are classified in broad

terms as either natural or man-made. Further subdivision of natural fibres leads to

animal (e.g. keratin fibres), vegetable (e.g. cellulose) and mineral fibres.

The transfer of hair and textile fibres can be compared to discover whether or not there

is a link between two people, or a person and a scene.88

Fibres located on objects used

in crime, such as vehicles and weapons can also be of significance.89

90

The persistence

of fibres at crime scenes is easily recognised by the fact that they are ubiquitous in

nature.9

26

A well cited and highly publicised case in forensic science involving fibre evidence is

the “Wayne Williams and the Atlanta Child Murders” trial of December 1981-February

1982.13

15

This case was significant because before this trial, fibre evidence had not

played such an important role in any case involving so large a number of murders.91

Associations were made between fibrous debris removed from the bodies of 12 murder

victims and objects in the immediate, everyday surroundings of Wayne Bertram

Williams. Peculiar and uncommon fibres consistent with these being used in carpets

and rugs originating from his home and automobiles, animal hairs from his dog and

African hair fibres originating from his scalp were recovered from the crime scenes.91

The amount of overwhelming and irrefutable fibre evidence was enough to convince the

jury beyond reasonable doubt that Wayne Williams was guilty and was ultimately

sentenced to serve two life sentences in prison.13

Human scalp hair is routinely collected from crime scenes as shown by their percentage

frequency from a variety of crimes (Figure 1.9).92

For example, human hair is

continually shed or deposited from the body through the normal hair-growth cycle (i.e.

proliferation (anagen phase), involution (catagen phase), and resting (telogen phase)). It

has been estimated that humans lose approximately 100 hair fibres per day;4 and

therefore to forensic investigators, a large percentage of the physical trace evidence

recovered from crime scenes is human hair. Natural fibres, such as cotton and wool

from garments and carpets respectively, usually „donate‟ or „transfer‟ fibres more

readily than synthetic or man-made fibres because they have a tendency to become

loose and fray.3 87 93-96

Therefore, characterisation of fibres, both natural and synthetic,

is a significant aspect of the forensic analysis of physical evidence.97

98

27

Figure 1.9 - A histograph indicating the relationship between the frequency with which

different types of trace evidence occurs in criminal cases. Adapted from Broad et al.92

1.4 Current Methods of Forensic Hair Analysis with the use of

Microscopy and DNA Analysis

1.4.1 Macroscopic Analysis

In the forensic examination of hairs it is important to begin with visual examination

followed by macroscopic examination of the morphology of individual hairs.83

Features

such as hair length, shape or form, root appearance, tip appearance, colour, disease

condition or abnormalities are all observed and measured.83

1.4.2 Microscopy

1.4.2.1 Optical Light Microscopy and Stereomicroscopy

In optical microscopy, four types of microscope are used to examine and compare hair

fibres from crime scenes. They include the stereomicroscope, the compound light/

polarising microscope and transmitted light comparison microscope and the scanning

laser confocal microscope.99

100

The stereomicroscope and the light microscope are

0

50

100

human hair

fibres

small

particles

Murder and

manslaughter

Assault

without rape

Burglary

Other

offences

Rape

Violent

Fre

quen

cy i

n c

asew

ork

(per

cen

t)

28

used for rapid preliminary analysis to determine species, racial origin and the somatic

(body location) origin.83

This is achieved through the analysis of hair characteristics

such as the medulla (i.e. classification) and cuticle, colour, spatial configuration,

diameter, cross-section, cortical cells, cortical fusi, birefringence and pigment features

are analysed.18 83

99

The scanning laser confocal microscope is a fluorescence-based

technique that allows the study of transverse cross-sections which is important in the

examination of human hair.100

The transverse cross-sectional shape may be of

assistance in determining the somatic origin or to assist in determining the ethnicity of

the donor.100

However, there are certain features present in hair such as heavy

pigmentation or the presence of an opaque medulla that can strongly interfere with the

laser beam or collection of fluorescence and have an adverse affect upon cross-sectional

image quality.100

The next phase of examination involves the direct comparison of questioned fibres from

the crime scene and known fibres from the suspect, side-by-side, using a comparison

microscope.99

101

When hair fibres are compared, it is difficult to associate questioned

and known sources because the morphological features differ from fibre to fibre on an

individual‟s scalp and from person to person. Morphological variation is an integral

part of natural growth.99

Conclusions drawn from such comparisons are therefore

subjective and rely upon the experience and skill of the examiner. Furthermore, the

evidence has to be independently assessed by a second examiner to give weight to the

primary assessment and reduce subjectivity of the conclusions.99

1.4.2.2 Scanning Electron Microscopy

SEM is a powerful tool for the forensic analysis of trace physical evidence such as

fibres, glass, paints and gunshot residues as it is a non-destructive means of examining

morphological characteristics of a material.102

Sampling preparation is simple and the

solid proteins of the hair fibre are relatively stable to the penetrating electron beam.103

In forensic hair fibre analysis by SEM, Taylor et al. state that “SEM highlights the

surface topography of the external cuticle layer in great detail with greater depth of field

than a stereomicroscope”.104

SEM is preferable to optical light microscopy as this also

gives poor topographic resolution of hair features.105

29

SEM analysis is useful for identifying the species of an unknown hair fibre as the cell

structure and thickness of the external cuticle layer is markedly different between

humans and animals.106

107

For example, the cuticle layer in fine Merino wool fibres is

normally one cell thick, whereas in human hair the cuticle is approximately 10 cell

layers thick.108

Also, the surface topography of the fibres is different.

However, for matching and identification of fibres of the same species, i.e. human hair

fibres, it was discovered that “SEM is difficult for comparison of human hairs because

the variability in the surface architecture, distribution and appearance of the scales

within one head are great, according to the natural and cosmetic history”.104

109

Additionally, there is considerable variability along the length of the fibre from root to

tip as a result of natural weathering processes and even due to grooming such as

brushing and combing.109

SEM is also limited by the fact that the morphological

features used to compare evidentiary and exemplar hairs are within the hair fibre, not on

the surface.20

Other studies involving SEM that have potential forensic applications concentrated on:

understanding the morphological variations of hair from different parts of the body110

,

analysing the damage of the cuticle as a result of weathering (i.e. sun bleaching or

photo-degradation65

111

, combing and brushing65

63

112

, shampooing65

113

, mechanical

stress)114

, and cosmetic treatments (i.e. permanent waving, bleaching and dyeing65

63

and

lacquered hair).104

1.4.3 Fibre Evidence from Burial Scenes

1.4.3.1 Burial of Hair Fibres

In homicide, murderers go to extreme lengths to avoid being apprehended and face the

repercussions of their actions. For example, after having slain their victim, perpetrators

will bury the body to disguise the human remains. Various locations and earth media

are utilised, such as remote forest or bush land, beaches, mangroves, backyards, garages

and cellars. The human body decomposes leaving behind the skeleton and hair fibres,

which, therefore, become important for the forensic scientist to assist in the

identification of the deceased. As the grave is being prepared, fibres from the

30

perpetrator can also be shed and remain buried until the victim‟s body is recovered.

The forensic examiner is therefore faced with examining fibres that have been exposed

to a variety of environmental conditions.

1.4.3.2 Environmental Weathering of Fibre Evidence

With the burial of hair fibres, Rowe states “very little is known about how

environmental conditions alter hair morphology”.20

However, several studies have

considered the effect of microbial attack on the identification and comparison of hairs.23

115-119 Serowik et al. and Kundrat et al. performed investigations whereby human hair

fibres were buried in garden soil for periods ranging from one to six months.116

119

The

buried hairs were exhumed and compared microscopically with hairs from the same

individual that had not been buried. Both those studies reported the tunnelling or boring

of the hair shafts by keratinolytic micro-organisms such as fungal hyphae. Serowik et

al.116

discovered up to four different types of fungal growths which were found to be

associated with the buried hairs.

DeGaetano et al.120

have also reported fungal tunnels in the hair fibre from a buried

body of a murder victim. SEM examinations revealed that the fungal hyphae had no

preference in the site of penetration, entering under the free edge of the cuticular scales

or directly through the scale surface. The damage caused by the fungal hyphae was

therefore random. They also observed the development of small cavities or vesicles

possibly caused by shrinkage in both the medulla and the cortex of the buried hairs.

Some buried hairs showed total destruction of their shafts at random locations.

Furthermore, the authors observed the appearance of darkened “necked” regions on the

shafts of buried hairs. The darkening of the hairs in these areas are artefacts resulting

from the etching of the shafts of the hairs as they are progressively destroyed by micro-

organisms. The general conclusion was that the bio-deterioration of hair in a soil

environment is likely to cause problems in forensic hair examinations.

31

1.4.4 DNA Analysis

Human DNA is the genetic “blueprint” material in the cell nucleus and in extra-nuclear

organelles of the cell, known as mitochondria (singular: mitochondrion) that is

responsible for determining our physical characteristics.121

Excluding identical twins,

no two people have the same genetic code, and thus DNA is unique to the individual.

From the forensic perspective this is most important as it provides a means for

association.121

Common sources/origins of DNA containing material most frequently found at crime

scenes are spattered blood, saliva, skin, seminal fluid and more importantly, hair fibres

which are present as a result of crimes of a violent nature. As hair is the most common

form of biological forensic evidence found at a crime scene, it is potentially a valuable

source of DNA for forensic analysis.122

1.4.4.1 DNA Analysis of Human Hair Fibres

As DNA is unique to the individual, DNA comparisons can form highly significant

associations between known and unknown hair samples.17

However, hairs contain

extremely small quantities of DNA.123

With hair fibre evidence, two sources of DNA

are available for forensic analysis.17

Nuclear (nuc) DNA, i.e. the cells pertaining to the

hair root and surrounding translucent follicular tissue, (root sheath cells), are the

optimum source of DNA.17

A hair fibre with its root attached is evidence that the hair

has been forcibly removed from the head. Unfortunately many, if not most human hairs

recovered from crime scenes (ca. 90 %) are in the telogen phase (i.e. the resting phase

of the normal hair growth cycle where the hair is naturally shed), and thus will not

contain a growing root or adhering tissue.124

Telogen hair can be of three types: (1)

club root without any soft tissue remnant (most common), (2) club root with a small

amount of soft tissue attached, and (3) club root with a large amount of soft tissue

attached.125

Hair roots with soft tissue remnants have been considered to contain some

cells with nucDNA.125

Andreassson et al.126

performed a study to investigate the

nucDNA content in anagen versus telogen hair fibres. The first centimetre of plucked

hairs contained an average of 25, 800 nucDNA copies while no nucDNA copies were

detected in the first centimetre of shed hairs.126

32

Even if sufficient amounts of DNA were extracted from hair, the DNA are not always

successfully amplified by the polymerase chain reaction (PCR), suggesting the presence

of PCR inhibitors (e.g. melanin, hair dyeing and sunlight oxidation) in the extracted

samples.127 128

By typing DNA from telogen hairs a loss of signal is typically observed

with larger STR (Short Tandem Repeats) fragment sizes due to the fact that the DNA

has been fragmented into small pieces during hair development.129

Hence, in most

cases, they are unsuitable for nucDNA analysis.7 123 130 131

Therefore, newly designed

STR systems for shorted amplicons sizes needed to be used.124

Over the past decade,

some laboratories have developed improved extraction methods and miniSTR kits

(short-amplicon PCR) to increase the typing chance of highly degraded hair.128

In 2001,

Hellmann et al.132

used a series of single STR typing steps while the extracted DNA

from the hair was fixed onto a membrane during consecutive PCR reactions. In 2010,

Bourguignon et al.125

proposed a new screening test to visualise DNA with 4-6-

diamidino-2-phenylindole (DAPI) which is a fluorescent molecule that binds onto

double-stranded DNA, between A and T base pairs.125

The use of a fluorescence

microscope makes it possible to count the visible nuclear DNAs and quickly discard

hairs less suitable for STR-typing, thereby focusing the attention towards hairs with the

greatest potential for results.125

1.4.4.2 Mitochondrial DNA

In those instances where telogen phase or naturally shed hairs are present, the analyst

then becomes interested in isolating DNA from the mitochondrial cells in the hair shaft.

For the analyst, this is a rich source of DNA because there are hundreds of mitochondria

and thousands of copies of mtDNA in each cell. Human mtDNA is an extra-

chromosomal, closed circular, organelle-specific genome consisting of approximately

16.5 kb (kilo-bases). The mtDNA genome consists of coding sequences for 2 ribosomal

RNAs, 22 transfer RNAs, 13 proteins and a non-coding region (1,100 base pairs), called

the displacement loop (D-loop). This non-coding region has the forensic potential as

this is where most of the sequence variation between individuals is located.

MtDNA was first introduced as evidence in Tennessee v. Ware133

in 1996; it has now

been applied in hundreds of cases.134

However, as with forensic microscopy

examinations, DNA analyses also suffer from limitations in that (1) they are not as

33

informative about the characteristics regarding the species or race associated with the

fibre, (2) mitochondrial (mt) DNA is inherited through the maternal lineage and (3)

adverse environmental factors (e.g. burial and degradation, contamination from

exogenous sources of DNA) affect the quality and quantity of DNA obtained from

biological samples, such as hair, because it is not well protected. This is in contrast to

DNA originating from forensic biological samples such as teeth.5

17 135 However,

several studies have demonstrated that it is possible to successfully decontaminate

modern hair shafts that have been contaminated with human saliva and blood.7 136

Gilbert et al.137

suggest that the survival of mtDNA in degraded hair samples and its

protection from external sources of contaminant DNA derives from the unique manner

in which hair grows during life. As the precortical cells keratinise to form the cortex,

they undergo loss of cell cytoplasm, organelle destruction and dehydration.137

This

apoptosis, associated with the programmed terminal differentiation of cortical

keratinocytes is a characteristic which is the protracted retention of organelle integrity,

most specifically mitochondrial integrity.138

The protracted maintenance of

mitochondrial membrane integrity may be more likely to protect the mtDNA.137

Additionally, the hydrophobic nature of the proteins in the cuticle and the keratin

packing of the cells helps provide a impermeable seal around the hair cortex and

suggests a plausible explanation as to how samples resist the penetration of contaminant

DNA.137

Despite some of the limitations, hair presents a useful source of mtDNA in forensic and

ancient DNA analyses.135

It is believed that the majority of post-mortem DNA damage

directly hinders PCR amplification, through events such as inter-strand cross-linking

and fragmentation.139

However, a small proportion of the damage does not hinder

amplification, but results in the generation of miscoding lesions. These miscoding

lesions can potentially provide misleading results in genetic analyses that rely on

directly amplified sequences from samples containing low levels of DNA.140

Histological screening of hair samples prior to mtDNA analysis has helped to alert

researchers to the possibility of such errors.139

During the past 7 years, the forensic community has addressed the requirement to

develop fast and reliable screening methods for mtDNA analysis.141

The DNA

quantification methods used prior to the development of real-time quantification were

34

often not sensitive enough for the trace amounts of DNA present in the types forensic

materials encountered today.126

In the past few years, the introduction of high-

throughput sequencing techniques for mtDNA analysis are currently in use, including

pyrosequencing , LINEAR ARRAYTM

and TaqMan® analysis, and has vastly improved

the yield from source materials as well as being more cost- and time-effecient .126 134 142

The analysis of SNPs (Single Nucleotide Polymorphisms) is characterised by primer

design that results in the analysis of short DNA fragments that are more stable against

degradation and therefore more successful when applied to even heavily damaged

mtDNA.141

The analysis of hair is a challenge for both the forensic microscopists and biologists

involved. Microscopy is subjective and provides only circumstantial evidence. In

addition, optimal sources of DNA (nucDNA) are less common, forcing biologists to

isolate mtDNA. However, in Queensland Australia (John Tonge Centre, Brisbane)

mtDNA is not extracted from the hair and the analysis is expensive.143

Efforts have been made to discriminate hair through chemical analysis, which includes

monitoring dye components, trace elements, proteins and the surface components

(lipids) of human scalp hair.17

However, over a decade, the main focus or drive by a

research group at Q.U.T. (Brisbane, Australia), has been towards utilisation of

vibrational spectroscopy, namely IR22-24 26

and more recently NIR27

spectroscopy for

structural elucidation. These techniques facilitate information on the molecular level

about the nature of the hair fibre.

1.5 Vibrational Spectroscopy

Biological systems consist of interacting chemical compounds, and the most important

structural and functional role is played by molecules. Molecules consist of atoms which

have a certain mass and which are connected by elastic bonds. As a result, the bound

atoms can perform periodic motions where the atoms alternately move towards and

away from each other i.e. they vibrate.144

35

In spectroscopy, the electromagnetic radiation travels as an oscillating magnetic field

perpendicular to an oscillating electric field with an energy and wavelength which is

described by the following equations145

:

ΔE = hν Equation 1.1

where;

ΔE = Energy (kJ mol-1

)

h = Planck‟s constant 6.625 x 10-27

kJ sec

υ = the frequency of light sec-1

Hertz (Hz)

and;

λ = c/ν Equation 1.2

where;

λ = the wavelength of the electromagnetic wave (cm)

c = the velocity of light 3 x 1010

cm sec-1

υ = the frequency of light sec-1

Hertz (Hz)

A wavenumber is defined as;

= 1/λ (cm-1

) Equation 1.3

Atoms of a molecule vibrate with a definite frequency that depends on the mass of the

atoms, the force of their binding and the structure of the molecule. The molecule will

absorb incident radiation at characteristic wavelengths corresponding to the energy of

the molecular vibrations, providing that a change in dipole moment occurs with the

vibration.146

Processes of change, including those of vibrations and rotations associated with infrared

spectroscopy, can be represented in terms of quantised discrete energy levels E0, E1, E2,

etc. Each atom or molecule in a system must exist in one of these levels. In a large

assembly of molecules, there will be a distribution of all atoms or molecules among

36

these various energy levels. The latter are a function of an integer (i.e. the quantum

number) and a parameter associated with the particular atomic or molecular process

associated with that state.146

At a specified temperature, the molecules that make-up a system of oscillators is in the

state of dynamic equilibrium determined by the Boltzmann energy distribution.

Whenever a molecule interacts with radiation, a quantum of energy (i.e. a photon) is

absorbed. The energy of the quantum of radiation must exactly fit the energy gap E1-E0

or E2-E1, etc. Hence, the selection rules must be obeyed. The requirement is that the

transitions be quantised and the transitions between the respective levels are

probable.146

If one oscillator passes to a lower state, another one will pass from a lower to a higher

state to maintain the equilibrium. Thus the energy promotes the molecule from the

ground state (E0) to the excited state (E1). Hence, the frequency of absorption of

radiation for a transition between the energy states E0 and E1 is given by146

:

υ = (E1 – E0) / h Equation 1.4

This can be represented on an energy diagram as a transition of the oscillator from the

ground state to the excited state (absorption of energy) (Figure 1.10)

Excited State

Ground State

Figure 1.10 – Absorption of energy for a vibration where the molecule is promoted

from state E0 to state E1 and the molecule in the higher vibrational state (E1) dropping

to the lower vibrational state (E0) emitting radiation of ΔE.

E0

E1

ΔE

37

1.5.1 Infrared Spectroscopy

1.5.1.1 Infrared Absorptions

Under normal conditions, the population ratio of a molecule is steady and increases with

temperature. Incident radiation stimulates transitions between vibrational levels. The

energy of most molecular vibrations corresponds to that of the mid-IR region of the

electromagnetic spectrum. This includes radiation with wavelengths () between 2.5

m and 25 m, which correspond to a wavenumber range of 4000-400 cm-1

.147

Reiterating, a transition can occur only if the dipole moment of a molecule is altered.

This is the selection rule for infrared spectroscopy.146

As a consequence, during the

vibration, the distribution of electric charge in the molecule must change. The negative

charge deriving from the electron cloud around the positive charge of the nucleus

frequently gives rise to a permanent dipole moment μ, (Equation 1.5):

μ = Q r Equation 1.5

where;

μ = dipole moment, debye, D, (statcoulomb centimetre, statC cm 10-18

)

Q = charge (statC, 10-10

)

r = distance between the charges (angstrom, 10-8

cm)

Infrared absorptions are not infinitely narrow with several factor contributing to the

broadening.146

The Doppler effect, in which radiation is shifted in frequency when the

radiation source is moving towards or away from the observer. The collisions between

molecules contribute to band or pressure broadening. Another source of band

broadening refers to the lifetime of the states involved in the transition. The energy

states of the system do not have precisely defined energies and this leads to lifetime

broadening. The relationship between the lifetime of an excited state and the bandwidth

of the absorption band associated with the transition to the excited state is a

consequence of the Heisenberg Uncertainty Principle.146

38

1.5.1.2 Infrared Modes of Vibration

A molecule can be looked upon as a system of masses joined by bonds with spring-like

properties. Polyatomic molecules such as keratin containing many atoms (N) which

have 3N degrees of freedom.146

In general, a molecule can only absorb radiation when the incoming infrared radiation is

of the same frequency as one of the fundamental modes of vibration of the molecule.

However, overtones and combination modes of vibration also occur.

Molecules have a number of vibrational modes that give rise to absorptions. These

vibrations include the stretching and bending modes.148

The stretching vibration is

associated with a motion of atoms causing elongation and shortening of the chemical

bond. In Multi-atomic systems the motion can be classified as either symmetric or anti-

symmetric in nature. Symmetric molecules will have fewer infrared-active vibrations

than asymmetrical molecules. Symmetric vibrations are generally weaker than

asymmetric vibrations since the former will not lead to a change in dipole moment.

A bending (scissoring) mode is an in-plane movement of atoms during which the angle

between the bonds changes. The bending vibrations can be classified as: (1) rocking

vibrations, which involves atoms swinging back and forth in phase in the symmetry

plane of the molecule; (2) wagging vibrations, is an in-phase, out-of-plane movement of

atoms, while other atoms of the molecule are in the plane and; (3) twisting vibrations, is

the movement of the atoms where the plane is twisted. For example, the localised

vibrations of the methylene group (Figure 1.11)147

:

C

H H

C

H H

C

H H

C

H HC

H H

C

H H

Figure 1.11 – Localised vibrations of the methylene group highlighting the symmetric

and anti-symmetric stretches, and the bending/scissoring, rocking, twisting and

wagging vibrations respectively.

39

In human hair and wool keratin, the peptide bond is the most abundant.149

The atoms

involved in this bond give rise to a number of vibrational bands that can be observed in

the IR spectrum of -keratin (Figure 1.12). In the wavenumber region of interest for

this investigation (1750-800 cm-1

), the major characteristic absorptions of the peptide

bond are the Amide I (1690-1600 cm-1

), Amide II (1575-1480 cm-1

), and Amide III

bands (1320-1210 cm-1

).

O

R

N

H

R

O

R

N

H

R O

R

N

H

R

Figure 1.12 – Modes of Vibrations for the Amide I, Amide II and Amide III bands

respectively for -keratin protein.

Other modes of vibration that are present in such a spectrum include the amino acid side

chains which have C-H deformations (1471-1460 cm-1

), CH2 and CH3 deformations

(1453-1443 cm-1

and 1411-1399 cm-1

), and the cystine oxide stretches which consists of

the asymmetric and symmetric cysteic acid (1171 cm-1

and 1040 cm-1

) symmetric

cystine dioxide (1121 cm-1

), and cystine monoxide (1071 cm-1

) stretches.

40

1.5.2 The Fourier Transform Infrared Spectrometer

Fourier-transform infrared spectrometers are used and have improved the acquisition of

infrared spectra. The schematic diagram, Figure 1.13, represents the Michelson

Interferometer. Radiation from a broadband source (e.g. globar) strikes the

beamsplitter. Some of the light is transmitted to a movable mirror and some of the light

is reflected to a stationary mirror. The moving mirror modulates each frequency of light

with a different modulation frequency. In general, the paths of the light returning from

the stationary mirror and the moving mirror are not in phase. They interfere

constructively and destructively to produce a pattern called an interferogram.148

150

The

interferogram contains all the frequencies which make up the IR spectrum. The

interferogram is a plot of intensity versus time (i.e. a time domain spectrum). By

performing a mathematical operation known as a Fourier Transform, the interferogram

can be decomposed into its component wavelengths to produce a plot of intensity versus

frequency, i.e. an IR spectrum. 148

150

41

Figure 1.13 - A schematic diagram of the Michelson Interferometer. Adapted from 146-

148

Fixed Mirror

Source

(Broadband Light)

Beamsplitter

Sample

Detector

Moving Mirror

Computer

IR Spectrum

42

1.5.2.1 Fourier-Transformation

The essential equations for a Fourier-transformation relating the intensity falling on the

detector, I(δ), to the spectral power density at a particular wavenumber,

, given by B(

), are as follows146

:

I(δ) = )2cos()(0

B d

Equation 1.6

which is one half of a cosine Fourier-transform pair, with the other being:

B(

) =

dI )2cos()( Equation 1.7

Equation 1.6 shows the variation in power density as a function of the difference in

pathlength, which is an interference pattern. Equation 1.7 describes the variation in

intensity as a function of wavenumber.

1.5.2.2 Advantages

FT-IR instruments have several significant advantages over older dispersive

instruments.146

1. Multiplex advantage (Felgett) – Improvement in the signal-

to-noise ratio (SNR), proportional to the square root of the

number of resolution elements.

2. Throughput advantage (Jacquinot) – The total source output

can be passed through the sample continuously, resulting in

a substantial gain in energy at the detector, translating to

higher signals and improved SNRs.

3. Co-addition of scans – Increase SNR by signal-averaging,

proportional to the square root of the time, as follows:

SNR α n1/2

Equation 1.8

43

4. High scan rate – The mirror has the ability to move short

distances rapidly to acquire spectra on a millisecond

timescale.

5. High resolution – By closing down the slits, a narrow band

is achieved.

6. Laser Referencing (Connes Advantage) – By using a

Helium-Neon laser as a reference, the mirror position is

known with high precision.

7. Negligible stray light – The detector responds only to

modulated light.

8. Powerful computers – Advances in computers and new

algorithms have allowed for fast Fourier-transformation.

1.5.3 Forensic Investigations of Human Hair Fibres using FT-IR Spectroscopy

Across the major scientific fields, biological human hair fibres have been studied for a

number of key purposes, i.e. for medical, environmental, cosmetic and more

importantly, for forensic sciences. As indicated previously, hair fibres from questioned

or unknown origins that are located on the victim and/or the immediate surroundings are

taken as corroborating evidence to link a suspect to a crime.

In the mid 1970s, criminalists were aware that dyed and bleached hairs could be

distinguished from untreated hairs by light microscopy.151

As mentioned earlier, this

technique involves identifying and matching the morphological features of human hair

fibres using known and unknown sources. However, the FT-IR spectroscopy facilitates

matching the chemical structure of identified and questioned fibres utilising structural

elucidation.

FT-IR Spectroscopy is a technique chosen for its sensitivity to the conformation and

local molecular environment of molecules including that of the biopolymers. It has

been suggested that “infrared spectroscopy is a powerful technique for the forensic

examination of fibres”3, and that “FT-IR analysis can provide rapid and specific

chemical information at the molecular level about the nature of the fibre and its

44

composition”.152

In early investigations in the late sixties, FT-IR spectroscopy had

been utilised to study the effects of oxidative treatment on human hair fibres.153-156

Much later in 1985, in the first forensically directed applications, Brenner et al.

performed an investigation on untreated and bleached hair fibres with the use of FT-IR

spectroscopy that utilised a diamond anvil cell to obtain transmission spectra.47

For the

bleached hair fibres, the authors discovered the presence of a peak at 1044 cm-1

which

was attributed to the symmetric stretch of cysteic acid. As a result of this study it was

suggested that “this peak may be used to differentiate treated and untreated hair

samples”. Ohnishi et al. furthered this study by analysing permanently waved hair

fibres.157

In this study, it was determined that the concentration of cysteic acid and

random damage patterns increased from root to tip depending on the frequency of

permanent waving.

In 1991, Hopkins et al. decided to investigate other IR absorptions of keratin by

examining the ratio of the Amide I to Amide II bands to characterise human hair.158

However, the spectra did not appear to have sufficient discriminatory value for forensic

use showing little or no difference in the Amide I/II ratio that could be correlated to

gender, age, and hair colour. The final statements in this study were important - “If such

differences do exist and can be detected by IR spectroscopy, they must be more subtle

than the simplistic technique used in this study (ratio differences)”.158

Finally, in 1994, Bartick et al. used FTIR-ATR Spectroscopy to investigate the presence

of hair spray on the hair fibre by subtracting the spectrum of an uncoated hair fibre from

a coated one to reveal the characteristic absorptions of the hair spray.159

As a result,

subtraction will be a tool used in this study.

Therefore, in summary, earlier FT-IR spectroscopic investigations showed some

promise for the forensic analysis of human scalp hair fibres. It was possible to

discriminate between untreated and cosmetically treated fibres through visual inspection

of the spectra. The prominence and intensity of the SO3- vibrational band at 1040 cm

-1

was strong evidence indicating that the disulphide bond (S-S) had been cleaved and

subsequently oxidised to cysteic acid residues by hydrogen peroxide during the

45

bleaching process. Unfortunately for the criminalists, no further discrimination was

possible.

Several years later, Panayiotou22

endeavoured to apply FT-IR Micro-spectroscopy for

structural elucidation. The spectra in this study were interpreted with the aid of

Chemometrics. This approach had not been previously applied to the study of hair

fibres. This amalgamation proved to be a very powerful one. As a result of this

research, human scalp hair fibres could be discriminated on the basis of 152

:

(a) the section of the fibre sampled, i.e. root, middle and tip,

(b) section of the head where the fibre originated (e.g. left, right, top, middle and back),

(c) gender,

(d) untreated vs. cosmetically treated hair,

(e) treatment vs. multiple treatment and

(f) black Asian hair vs. black Caucasian hair.

Furthermore, unknown hair samples (i.e. blind samples with their history being

withheld from the author) were submitted to a reference spectral database to assess the

validity of the technique. It was discovered that this method predicted correctly

approximately 83 % of samples with respect to the history of the unknown fibres.

1.5.3.1 Applications of Chemometrics to Forensic Science

In forensic and criminalistic studies, PCA has been utilised to aid and solve numerous

problems in different forensic science disciplines.160

161

The earliest applications in

1989162

and the mid-late 1990s163

164

concerned investigations in morphometry (i.e.

skeletal gender determination of the skull and scapula), and in areas adjacent to forensic

medicine (i.e. regional differences in alcohol and fatal injury165

) differentiation between

sharp force homicide and suicide.166

In Australia (with collaboration with the Royal Canadian Mounted Police), forensic

arson studies using chemometrics involved the classification of unevaporated premium

and regular gasoline167

and differentiation of polycyclic aromatic hydrocarbons on the

basis of GC-MS data.168 169

46

Textile fibre studies performed by Kokot et al. have demonstrated that Diffuse

Reflectance Infrared Fourier Transform spectroscopy (DRIFTS) taken from dye

mixtures extracted from textile samples, cluster and match according to their sampling

area on the test material.170

Gilbert et al. established that it was possible to differentiate

between cotton-cellulose fabrics on the basis of the fabric dye, fabric type and level of

textile processing.171

With continued study on cotton fabrics, Kokot et al. were able to

show that fabric samples containing different states of a reactive dye and samples dyed

with differently coloured unfixed reactive dyes could be discriminated on the basis of

their DRIFTS spectra.172

Keen et al. reports that spectra from the same fibre type

(polyester and polyamide) from different manufacturers have very similar spectra but

can be separated using PCA.173

In two papers concerned with document examination, Thanasoulias et al. 160

and Kher et

al. 161

were able to discriminate between different blue and black ball-point pen inks on

the basis of their UV-Vis spectra and HPLC chromatograms respectively. Novel

approaches in ballpoint ink analysis involved discrimination of ink-lines from 10 pens

using non-destructive luminescence spectroscopy and PCA.174

Thanasoulias et al. were

able to discriminate between 44 soil samples from five different areas, also on the basis

of their UV-Vis spectra of the acid fraction of humus.175

Brody et al. have published results on the discrimination of dentine from six

mammalian species and differentiated dentine from bone and cementum to counteract

the illegal trade of African and Asian elephant ivory and identify legitimate and „fake‟

ivory respectively.176

Several investigations have been carried out by forensic laboratories concerned with

linking seized illicit amphetamine and heroin samples to the source (common batch) of

production177

178

, classification on the basis of cocaine concentration179

, and

differentiation between illicit methaqualome containing tablet formulations.180

47

1.5.3.2 Previous Investigations using FT-IR Spectroscopy and Chemometrics

Panayiotou expanded her studies to include a wider range of -keratin fibres, namely

those from animal fibres.24

In later work, Panayiotou developed a forensic protocol,

which as defined by Barton is “a systematic approach for the analysis of unknown hair

fibres from crime scenes with the use of FT-IR Spectroscopy”.23

The spectral evidence

could then be used in conjunction with current methods of examination, such as

microscopy and DNA analysis. It was proposed that the integration of these three

techniques would improve identification of a hair „profile‟, giving information on the

morphological, molecular and genetic levels.

The scope of this work was broadened by Paris25

, adding yet another dimension to the

ever growing area of forensic hair fibre analysis by FT-IR spectroscopy. Paris aimed to

match and discriminate individuals after the hair fibres had been environmentally

weathered. This is important to consider as hair fibres can be discovered in a wide

variety of environmental conditions. The hair fibres of selected individuals were

subjected to different surroundings (i.e. sand, soil and mud, which is assumed to range

from moderate to harsh conditions respectively) for various time intervals. These media

were chosen as they represent potential burial sites for the disguise of human remains in

homicide cases.

From Paris‟s study, it was apparent that only approximate matching of individuals can

be accomplished after the fibres have been both weathered and cleaned. From the

forensic perspective this becomes a problem for positive identification of an individual.

1.5.3.3 Limitations to the Previous Investigations

Through a critical examination, significant limitations could be attributed to the

previous investigations carried out by Panayiotou and Paris.23

First and foremost, the

authors did not have a large data set. Fibres were only sampled from two major races

(i.e. Caucasian and Asian), whilst the third major race (i.e. African or African-type) was

neglected. Although on the macroscopic level an African-type hair appears obvious, it

cannot be so easily distinguished from pubic and beard hair which also has crimp.

Therefore, the conclusions on the discrimination of individuals formulated by

Panayiotou22 24

and Paris25

can only apply to Caucasian and Asian hair fibres. If

48

unknown African-type hair fibres were present at a crime scene, the forensic protocol

would be rendered inadequate because the analyst would not be able to determine on

what basis the questioned fibres are discriminated, therefore throwing the spectral

analysis into jeopardy.

However, the most significant limitation concerned the sampling preparation of the hair

fibre prior to spectral analysis. Spectroscopically, hair fibre investigation can involve

the employment of a number of IR sampling techniques such as the traditional FT-IR

Micro-spectroscopy (previous studies)22 24 25

, FTIR-Photoacoustic spectroscopy (FTIR-

PAS)181 182

, Raman spectroscopy45 183-185

, Near-Infrared spectroscopy (NIR)27

and the

more novel (with respect to its involvement in this subject matter), FTIR-ATR

spectroscopy.23 26

However, in general, these techniques have different spectral sampling methods as well

as different spectral resolution and chemical information (IR vs. Raman) that can be

extracted. This of course becomes an issue from the forensic perspective in that the

investigator/s must draw as much information from the fibre that is physically possible,

with acceptable precision and accuracy, to formulate conclusions that are beyond

reasonable doubt for any later convictions and sentencing that may be made.

In the previous investigations the spectra were recorded in transmittance. As hair fibres

absorb IR radiation strongly, they needed to be rolled and flattened to reduce lensing

effects53

, enhance the signal to noise ratio186

, and decrease the path length of the IR

radiation and subsequently the absorbance, as given by the Beer-Lambert law150

(Equation 1.9):

A = bc Equation 1.9

where:

A = Absorbance

= molar absorptivity (M-1

cm-1

)

b = pathlength (cm)

c = concentration of the sample (M)

49

Panayiotou22

and Paris25

employed SEM to determine the approximate number of rolls

required to flatten the fibre which left minimal physical damage, while still allowing

sufficient transmission of the IR radiation through the fibre. Nevertheless, it was clear

from the SEM images that the rolling technique was relatively destructive to the hair

fibre. After four rolls of an untreated fibre, the hair began to stretch and produce splits

and voids that ran along the length of the fibre. The damage was far greater with a

bleached hair fibre after four rolls due to decreased structural stability. Robbins

reported that when a fibre is stretched there is a transformation of the secondary

structure of the protein from the -helix to the -pleated sheet arrangement, also known

as -keratin.11

After 15 and 10 rolls of an untreated fibre and treated fibre respectively,

the hair was virtually destroyed and useless for analysis. Although a satisfactory number

of rolls were selected, in general the spectra recorded were of poor quality. The spectra

suffered from what Kirkbride3 and Robertson

187 describe as “peak saturation” or “band

saturation”, where the Amide I and Amide II bands of each spectrum were apparently

saturated, appearing as broad flattened peaks. However, it should be noted that

application of chemometrics such as PCA reduced the influence of these broadening

effects by appropriate pre-treatment and stepwise extraction of the PCs.

Nevertheless, to avoid “matrix” or saturation effects to obtain good quality spectra, and

a better representation of the -keratin structure, Barton23

investigated the use of a

different IR sampling technique. As opposed to sampling in transmittance, the

information was collected from fibres using Attenuated Total Reflectance (ATR), which

is a reflection method.

1.5.4 Fourier Transform Infrared Spectroscopy - Attenuated Total Reflectance

Fourier Transform Infrared - Attenuated Total Reflectance (FTIR-ATR) Spectroscopy

otherwise known as Internal Reflection Spectroscopy (IRS), is just one of a wide range

of IR sampling techniques available and is a well known method for measuring IR

spectra.188-191

ATR was developed independently in the 1960‟s by Harrick and

Fahrenfort.189

FTIR-ATR spectroscopy historically has been used for samples which

are too thick for transmission measurements192-194

, finding widespread use in studies

which were concerned with the near-surface chemistry of forensic159

, biological and

50

industrial materials which encompassed both natural and synthetic fibres 51

52 159 181 184

185 195-204, paints

159 205

, adhesive tapes206

, coatings 207 208

, human body specimens53 209

210

,

insect cuticular proteins and chitin211

, polymers and rubbers191

212 213

and

pharmaceuticals.214

215

FTIR-ATR spectroscopy is based on the phenomenon known as Total Internal

Reflection (TIR) (Figure 1.14).188

216

In this sampling technique, infrared radiation is

directed into an internal reflection element (IRE), which is a medium fabricated of a

high refractive index crystalline material (eg. Diamond, ZnSe, ZnS, and KRS-5) and

transmits radiation in the spectral region of interest.196 215 216

The angle of the incident

IR radiation, θi, exceeds the critical angle θc. When this radiation strikes the interface

between the IRE and the sample composed of a lower refractive index, total internal

reflection is achieved.215

216

Figure 1.14 – Total Internal Reflection in Attenuated Total Reflectance Spectroscopy.

Adapted from188 196 215

.

Figure 1.15 – An evanescent wave that is produced upon Total Internal Reflection that

eventually penetrates the sample. Adapted from159 215

.

Evanescent Wave

Sample

Attenuated Total Reflection

A

C

B

IRE

51

This internal reflectance creates an evanescent wave that extends beyond the surface of

the crystal and penetrates only a short distance into the sample (Figure 1.15).159

215

216

As the sample absorbs IR radiation at certain frequencies, the resultant totally reflected

radiation will be attenuated (altered) in regions of the infrared spectrum where the

sample absorbs energy.215

216

The IR radiation exits the crystal and passes through the

spectrometer to the detector where the spectrum is recorded.191

The intensity of the evanescent wave whose electric field amplitude decays

exponentially with distance from the surface of the IRE crystal is given by188 215

:

E = Eoe-z/dp

Equation 1.10

where;

E = electric field amplitude

Eo = external electric field

-z = vector component of the evanescent wave

dp = depth of penetration

The depth of penetration (or sampling depth) for experiments involving ATR has been

defined by Harrick 188

“as the distance required for the electric field amplitude to fall to

e-1

of its value at the surface”, and is given by 188 217

:

dp = 2/1

2122

1

1

)(sin2

n Equation 1.11

where:

dp = penetration depth

λ1 = λ/n1 is the wavelength in the IRE

θ = is the angle of incidence with respect to the surface normal

η1 = refractive index of the IRE

η2 = refractive index of the sample

η21 = 1

2

the ratio of the refractive indices of the sample and the IRE

52

An IR spectrum using an ATR accessory is not identical to the spectrum obtained using

transmission.218

The ATR technique introduces relative changes in band intensity and

absolute shifts in frequency. The relative intensity change is well-known and easily

corrected using a simple algorithm in the (OMNIC) software (Equation 1.4)219

:

Ycorr = Y / dp Equation 1.12

where;

Ycorr = Corrected intensity of a data point (a.u.)

Y = Original intensity of a data point (a.u.)

dp = Depth of Penetration at wavelength λ

An advantage of ATR is that the penetration depth is dependent on these variables

mentioned earlier; therefore depth profiling studies are possible.53

202

The depth of

penetration remains relatively small, in the range of 0.05-0.12 (for most samples).220

In this investigation, measuring keratin spectra between 1800-750 cm-1

with η1 diamond

= 2.419 (at λ = 1000 cm-1

) and η2 human hair221

= 1.555 the penetration depth is

approximately between 1.30 – 3.06 µm. It must also be taken into consideration that the

pressure tower of the ATR accessory compresses the sample26

, increasing the diameter

of the fibre allowing the IR radiation to penetrate deeper into the fibre.

Hence, ATR is a powerful method as it is insensitive to sample thickness, permitting the

surface or near-surface analysis of thick or highly absorbing materials, i.e. α-Keratin

fibres51-53 159

195

196

53

1.5.4.1 Previous Investigations of Human Hair Fibres Utilising FTIR-ATR

Spectroscopy with the aid of Chemometrics and SEM

The research conducted by Barton23

with the application of ATR spectroscopy proved

to be successful, with reference to the proposed objectives. As a synopsis of a section

of the results obtained from that study, it was concluded from the spectral evidence that

FTIR-ATR Spectroscopy had a number of advantages over the earlier IR sampling

method, these included:

(1) The spectra that were produced were of better quality. FTIR-ATR avoids

excessive absorbance of IR radiation, which therefore also minimises the

“peak saturation” or “band saturation” (i.e. avoids the saturation of the

Amide I and Amide II bands).

A comparison of the -keratin spectra quality from the two techniques is shown in

Figure 1.16. The saturation of the Amide I and Amide II bands at 1650 cm-1

and

1530 cm-1

respectively, in spectra sampled by Micro-spectroscopy are well illustrated.

On the other hand, spectra sampled by ATR display Lorentzian/Gaussian line shape

with relatively sharp peaks. However, it must be taken into consideration that the ATR

technique samples only the cuticle and peripheral region of the cortex. More

importantly, there is no loss of chemical structural information as generally the spectral

profiles of the FT-IR Micro-spectroscopy and the FTIR-ATR methods are similar.

54

Figure 1.16 – A spectral comparison of -keratin spectra using FTIR Micro-

spectroscopy (blue line) and FTIR-ATR Spectroscopy (pink line).

8001000120014001600

Wavenumber (cm-1

)

Ab

sorb

an

ce (

a.u

.)Transmittance

ATR

Cystine Dioxide

C-H Deformations

Amide II

Amide I

Amide III

Cystine Monoxide

Cysteic Acid

55

(2) Essentially, the technique is economical on time. There is less

instrumentation set-up and sampling preparation is simple as opposed to

Micro-spectroscopy where the microscope has to be continually focused,

and the fibre has to be rolled several times and positioned on the

microscopic slide.

Thus, with FTIR-ATR, more spectra can be generated over a given time period which is

important in forensic science as most government crime laboratories (e.g. Queensland

Health Scientific Services) have an overwhelming back-log of criminal cases.222

(3) Sampling preparation is easy and considerably less destructive as opposed

to the rolling technique utilised by the previous investigations.

The rolling technique required a couple of centimetres of the fibre to be rolled, which

consequently stretched and split the fibre. The stretching of the fibre affects the

secondary structure of the protein from the -helix to the -pleated sheet/random coil

arrangement. With ATR, only a small point of the fibre is compressed by the pressure

tower.

1.5.5 Alternative FT-IR Sampling Techniques for Analysing α-Keratin Fibres

1.5.5.1 FT-IR Photoacoustic Spectroscopy (PAS) of Human Hair Fibres

Studies of keratin have involved FT-IR Photoacoustic Spectroscopy (PAS). This

particular technique involves generating signals as a result of the absorption of radiation

by the sample, producing a periodic temperature oscillation within the optical

absorption depth.181

This technique allows scientists to discriminate between the surface and the underlying

layers of solid materials, as only the photoacoustic signals generated within the thermal

diffusion length are detected. The sampling depth or rather the thermal diffusion depth

(µs), is dependent upon both the optical velocity (ν) of the interferometer and the

56

wavenumber (cm-1

) of the infrared radiation according to the Rosencwaig-Gtersho

theory.223

In 1994, Jurdana et al. performed depth profiling studies to distinguish the between the

cuticle and cortex layers of wool (Lincoln, Drysdale and Merino) and Caucasian hair

fibres.181

FT-IR/PAS spectra were obtained at both low and high optical mirror

velocities between 0.0256 to 2.56 cm s-1

. These spectra exhibited significant

differences in the fingerprint region (1000-2000 cm-1

). At low optical velocities, all

types of fibre displayed a greater degree of overlap of the Amide I and II bands as

opposed to spectra obtained at high optical velocities. The authors suggested that the

behaviour for these differences were due to signal saturation, peak broadening and the

chemical composition between the cuticle and cortex.181

1.5.5.2 FT-Raman Spectroscopy of Human Hair Fibres

FT-Raman Spectroscopy has been used to study the chemical structure of human hair.45

Raman is a complementary technique to infrared; they are not identical as they are

governed by different selection rules. Whilst infrared relies upon a change in the dipole

moment of the molecule during the vibration, Raman on the other hand is dependent

upon a change in polarisability during the vibration which relates to the ease with which

the electron cloud can be distorted by the electric field of light.224

Hence, the FT-

Raman spectra for human hair exhibits some similar, however mainly different

vibrational information. This includes the Amide I (1655 cm-1

), υ(C=C) stretch (1585

cm-1

), δ(CH2) deformations (1450 cm-1

and 1315 cm-1

) and υ(C-C) skeletal stretches

(1129 cm-1

, 1084 cm-1

, 1060 cm-1

, 1041 cm-1

and 1003 cm-1

) and υ(C-S) stretches (745-

700 cm-1

trans, 670-630 cm-1

gauche). Williams et al.45

performed an investigation

concerning different human keratin biopolymers such as skin stratum corneum, callus,

hair and nail. The results illustrated that the FT-Raman spectra from human hair was

pigment dependent; blonde hair proving easier to analyse than dark hair due to

fluorescence.45

Fluorescence can be avoided with 1064 or 780 nm lasers with the

consequence of reduced sensitivity but can be improved using excitation wavelengths of

633 or 514 nm.24

57

Akhtar et al.183

carried out an investigation concerning the changes during bleaching

which showed the decrease of the cystine (S-S) disulphide links at 540 cm-1

, 525 cm-1

and 510 cm-1

which correspond to the trans-gauche conformation.

In summary, the alternative techniques suffer from a lack of important vibrational

information. Therefore, in consideration of these limitations, the spectra derived from

FTIR-ATR spectroscopy were sufficient to investigate single human hair fibres.

However, although the quality of the spectra has been appreciably improved through the

utilisation of FTIR-ATR Spectroscopy, the vibrational spectrum of human hair keratin

itself, particularly within the wavenumber range of 1750-800 cm-1

is extremely

complex. The spectral complexity is governed by the fact that there are a number of

vibrational bands, especially in the Amide I (1690-1600 cm-1

), Amide II

(1575-1480 cm-1

) and cysteine oxidation (1200-1040 cm-1

) region that are overlapped

and provide no further qualitative information.

Thus, as a consequence of the intricacy within this spectral region, much structural

information about the keratin protein remains hidden and non-participant in the IR

spectrum. By delving more profoundly into the unprocessed spectrum allows one to

justify their reasoning for identifying similarities or discrepancies between adjacent

spectra.

This complication can be solved through the use of a mathematical manipulation

method, by means of performing second derivative analysis on the IR spectra, which is

a process that has not been used by previous investigations, this rendering it a novel

approach.

1.5.6 Derivative Spectroscopy

The utilisation of differentiation to enhance the fine structure of empirical data was first

proposed by Lord Rutherford in the early 1920s.225

A electromechanical technique was

successful in obtaining the first derivative curve for the deduction of ionisation

potentials in mass spectrometry. However, with the achievement of this early

58

inspiration, the employment of the derivative methodology in spectroscopy did not

commence until the 1950s. Around that period, derivative spectroscopy had been

utilised in the field of UV-Visible Spectroscopy for resolving overlapping peaks and

was equally applicable to IR Spectroscopy.51

The application of derivative measurements has found practical use in many areas

where the interpretation of the conventional spectra is complex, attributed to a high

background signal or the superimposition of two spectral bands thus causing

interference.226

The advantages that the derivative mode carries is that it facilitates the

enhancement of the resolution between two overlapping bands; which assists

quantitative assay of mixtures; the suppression background (matrix interference) effects

to correct for systematic error; and improvement of fine spectral characteristics for

qualitative analysis.226

The manner in which derivative spectroscopy operates is that the rate of change of a

signal is recorded as a function of the wavelength or frequency.226

For a given

absorbance curve, the first derivative (dA/dλ) is the gradient of the original spectrum at

each wavelength. Further differentiation generates the second and higher derivatives:

2

2

d

Ad . . .

n

n

d

Ad

The general form of IR and Raman spectra have been shown to be characterised by the

Lorentzian function as given by227

:

A = Ao

1

2

2

31

Z Equation 1.13

where:

A = absorbance at wavelength λ

Ao = absorbance at λmax

Z = displacement (λ-λmax)

σ = standard deviation

59

The derivative profiles of Lorentzian curves are sharper than those of Gaussian curves

with the same amplitude and with the same full width at half maximum absorbance. By

computing the differentials of simple Gaussian and Lorentzian peaks, it can be seen that

the odd number derivatives exhibit a shift in the wavenumber of the peak whilst the

even numbered derivatives display the main peak at the original wavelength of

maximum absorbance.51

Successive differentiations of the signal obtained resolve any Gaussian or Lorentzian

component peaks masked by overlapping. However, as the derivative order increases,

the spectra become more complicated due to the presence of satellite peaks, thus second

derivative spectra are the most optimum.

1.5.6.1 Properties of Derivative Profiles

1. Resolution Enhancement

Differentiation of even order derivatives of both Gaussian and Lorentzian functions

results in a large reduction in bandwidth; the Lorentzian curves especially.226

In an

investigation carried out by Fell228

, it had been established that in regards to Lorentzian

curves, the full width at half maximum absorbance (FWHM) falls to less than 1/3 of its

zero-order value in second derivative mode.

2. Amplitude

With the utilisation of even-order derivatives, the amplitudes of the centroid peaks of

Lorentzian and Gaussian curves differ with increases of the derivative order, n, with the

Lorentzian curve being greater by an amount factorial n/2.226

3. Modes of Measurement

In derivative mode a number of methods of quantitative measurement exist where the

suitability of the technique depends on the profile obtained. The preference of any

particular measure of derivative amplitude for an analysis is governed by factors such as

60

the (a) presence and spectral characteristics of interference signals, (b) the useful linear

range of the derivative signal, and (c) the relative amplitudes of the various derivative

signals.226

The selection of an appropriate derivative order and measure is based upon deliberation

of „interaction‟ graphs.226

Hypothetically, in the analysis of a bi-component system,

derivative amplitudes are plotted against the concentration of the interfering

component.226

The ideal derivative measure is the one that yields an amplitude which

does not vary with the concentration of the interfering component.226

4. Satellite Interference

It has been established that as the derivative order increases, the number and amplitude

of the associated satellite peaks increases.226

Another feature of the satellite pattern is

that the displacement of the satellite peaks from the centroid peak is greater for

Gaussian curves than for Lorentzian of equal derivative order. Outlying satellite peaks

of Lorentzian bands are undetectable beyond approximately ±1.5σ (standard deviation)

whilst those of Gaussian bands are still just discernible at ±3.5σ.228

Hence, it can be

seen that in the higher order derivatives, peak resolution is enhanced, with the

concurrent significant increase in satellite peak interference, especially with Gaussian

curves.

5. Noise

It is apparent that the derivative modes provide a more characteristic profile of a

substance than does the corresponding zero-order spectrum.226

However, the presence

of noise reduces significantly the practical usefulness of the method. In electrical

instruments such as FT-IR spectrometers, three common types of noise exist, random

white noise; 1/frequency; and line noise.150

Random white noise, also known as

Gaussian noise, arises from the random motion of electrons in a circuit. Drift noise or

1/f noise, is greatest at zero frequency and decreases in proportion to 1/frequency.

Low-frequency noise, e.g. due to continuum background absorption or light scattering,

is rejected in the higher order derivatives, whilst high-frequency random noise results in

poor signal-to-noise ratios (SNR) compared to zero-order spectra.229

High-frequency

61

noise is a concern because even if it has a small amplitude compared to the true signal,

it constitutes a sharp spectral feature.226

Drift arises from causes such as slow changes

in instrument components with temperature and age and variation of power-line voltage

to an instrument.150

Line noise, also characterised as interference or whistle noise occurs at discrete

frequencies such as the 60 Hz transmission-line frequency or the 0.2 Hz vibrational

frequency.

In zero order spectra, the presence of noise is not noticeable; however it grows to be

more evident in the second order derivative profile. Proceeding then onto the fourth

derivative, the signal arising from the noise is of such a magnitude that it inhibits any

practical information to be interpreted from a spectrum.

A study carried out by O‟Haver et al.229

focused on the effects that random noise

impacts on the derivatives of Gaussian bands where the authors discovered that on

average the signal-to-noise decreases by a factor of approximately two with each

successive differentiation. However as a consequence, a balance has to be established

between the benefits of better resolution enhancement and reduction in systematic errors

resulting from the higher derivative orders and the higher signal-to-noise ratio of the

lower orders. Fortunately, Lorentzian peaks that are encountered in the infrared region

provide greater derivative amplitude and bandwidth, therefore the signal-to-noise ratios

are higher for the higher order derivatives.226

The effects of noise can be suppressed by the employment of various types of function

for smoothing spectra in digitised form. However, one must take into consideration

with smoothing functions that although the signal-to-noise ratio increases, there is a

simultaneous reduction in resolution.

Several methods exist for smoothing and derivative calculation, the functions based

mostly on the sliding average method.226

The Savitzky-Golay method is one of the

most common techniques that has been utilised in this investigation for the analysis of

second derivative spectra of α-Keratin proteins.

62

1.5.6.2 Generating Derivative Spectra: The Savitzky-Golay Method

The most common technique of calculating the second derivative is based on the

Savitzky-Golay method.230

This method is based on a convolution function procedure,

the nature of which is adjusted to yield the required degree of smoothing and order of

differentiation. The process calculates the first nine derivatives, where the algorithm

produces the least squares fit of the data to the selected polynomial.230

The simplest form of convolution to smooth fluctuating data is by using a sliding

average.226

This process takes a fixed number of points, adds their ordinates together,

and divides by the number of points to obtain the average ordinate at the centre abscissa

of the group.230

Subsequently, the point at one end of the group is dropped, the next

point at the opposite end added, and the process repeated.230

Mathematically, the smoothed value of the central datum, Y*

i , is taken to be the simple

average of a group 2n + 1 points distributed evenly around that central point given

by226

:

Y*

i = (Yi-n + … + Yi-1 + Yi + Yi+1 + … + Yi+n) / (2n + 1) Equation 1.14

If a weighted average is substituted for the simple average, then each Yj (j = i-n to i+n)

is multiplied by an analogous weighting factor Cj and the addition of CjYj is divided by

a normalising N, given by:

Y*

i = N

YCn

nj

jij

Equation 1.15

In the Savitzky-Golay algorithm, the weighting factors, Cj, are the integral coefficients

of a polynomial (i.e. convolution function) of second to sixth order. The first and

higher derivatives are produced by applying the coefficients of the differentiated

polynomial. The number of convolution points can range from five to 10,000, although

values greater than the number of points across a peak is not used. Only odd numbers

63

are used for the number of convolution points and even numbers are rounded up. The

greater the number of convolution points results in greater smoothing of the peak line

shape.

A complete set of tables for derivatives up to the fifth order for polynomials up to the

fifth degree, using averages taken over five to 25 points are presented in the Appendices

of the original paper by Savitzky-Golay (note: corrections to various arithmetic errors

are presented by Steinier et al. 231

) .

64

1.6 Aims and Objectives

Global Aim: To further the ongoing investigation concerning the identification and

discrimination of single, naturally occurring fibres namely human scalp hair with the

utilisation of FTIR-ATR Spectroscopy associated with Chemometrics and Multi-

criteria Decision Making techniques for data interpretation.

1. To collect human scalp hair fibres from males and females of Caucasian,

Asian and African-type backgrounds of a wide variety of ages. The

collected hair fibre samples also varied between untreated and chemically

treated hair fibres that have been subjected to different levels of cosmetic

treatments (i.e. from mild to harsh).

2. To persevere in the investigation concerning the expansion and

diversification of the provisional, unverified Forensic Protocol for analysing

single human hair fibres using FTIR-ATR Spectroscopy and Chemometrics

developed in previous studies. To achieve this a number of novel

approaches were utilised:

a) Derivative spectroscopy i.e. second derivative spectra to unravel the

complexity of the keratin spectra.

b) Spectral subtraction to determine the key spectral differences between

various types of fibre i.e. gender, and illustrate the underlying principles

for the separations and to assist the information gained from (a).

c) On the basis of (a) and (b) a novel investigation of potential

classification of hair spectra with the aid of various chemometrics

methods such as Fuzzy Clustering (FC), and PROMETHEE and GAIA

over alternate wavenumber ranges selected on the basis of the detailed

studies in (a) and (b).

65

3. To utilise the improved protocol to investigate a number of unremitting

issues that warranted further investigation that had not been considered in

previous studies:

a) To establish how African-type hair fibres fit the proposed method on the

basis of chemical treatment, gender and race.

b) To study various chemically treated hair fibres from minimal or mild

chemical treatment (i.e. cosmetic surface treatments such as gel and

hairspray, straightening with an iron, etc.) to harsh oxidative chemical

treatment (i.e. Bleaching and permanent dyeing).

c) To justify the basis of separation between male and female hair fibres with

supporting evidence of difference and second derivative spectra.

d) To assimilate the major IR spectral differences between spectra of

different racial origin, which are of the same hair treatment class/type and

same gender.

66

2.0 EXPERIMENTAL: MATERIALS AND METHODS

2.1 Collection of Fibre Samples

Human scalp hair fibres were donated by 66 people. The hair fibres (i.e. a minimum of

10 hairs from each individual) were taken at random locations from the scalp in the

telogen phase (i.e. as waste) and anagen/catagen phase (i.e. cut at the root) of the hair

growth cycles. Forty-six were current residents of Brisbane, Queensland, Australia, and

the remaining 20 were from Sugarland, Texas, United States of America. The fibres

were placed in plastic sealable sample bags and permanently stored in an air-controlled

environment (RH 65 % ± 2 %; 22oC ± 2

oC %) to minimise water adsorption/absorption.

Each person was requested to complete a survey form (Appendix I, p.290), giving

general particulars and more importantly specific information about the nature of their

hair (i.e. cosmetic treatments in the form of bleaching and dyeing, the use of hair

products, level of sun exposure, whether or not they swam and how frequently, etc.) that

would help aid the IR and Chemometric interpretation process. The samples were

diverse, ranging from individuals of different (1) race (i.e. Caucasian, Asian and

African-type), (2) gender (x Male and y Female), and (3) age (youngest 6 – 85 oldest)

and (4) types of chemical treatment/s.

2.2 SEM Analysis

Randomly selected untreated hair fibres were cut into approximately 1 cm samples,

positioned on carbon black sticky tape, then transferred to a metal grooved slug type

SEM mount (ProSciTech). The stubs were then coated in an SC500 Gold Sputter

Coater (BIO RAD Microscience Division) to prevent the sample from charging. SEM

images were obtained using an FEI QUANTA 200 Scanning Electron Microscope (FEI

Company, U.S.A.) at an accelerating electron voltage of 15.0 kV – 20.0 kV.

67

2.3 Cleaning Methodology

2.3.1 Revised IAEA Method for Cleaning Hair Fibres

The procedure was originally used by Cargnello et al.232

for the cleaning of

contemporary and well preserved historical hair samples in preparation for elemental

analysis.

The revised method233 234

involves sonicating the hair fibres in each solution for shorter

intervals to 10 minutes each to minimise the damage to the cuticle surface. Hair fibres

are transferred to a small glass vial and filled with high purity acetone (AR grade, Assay

99.5 % (min), Banksia Scientific Co Pty Ltd). The vial was transferred to a

MEGASON Ultrasonic Disintegrator (Figure 2.1) set to 20 kHz sonic intensity and the

fibre was sonicated for 10 minutes. The acetone was decanted, and the fibre was rinsed

with HPLC-grade water (18 M resistivity). This was subsequently decanted, filled

again with HPLC-grade water and sonicated for 10 minutes. Finally, the fibre was

rinsed and sonicated in de-ionised water for 10 minutes in a glass vial.

Figure 2.1 - A photograph of the MEGANSON Ultrasonic Disintegrator that was used

to sonicate the fibres for this study.

Sonicator

Sonic Intensity

Control

68

Once the fibres had been cleaned, they were transferred to an open petri-dish and then

placed in a plastic desiccator (filled with silica desiccant), under vacuum and dried for

two days. After this period, the fibres were transferred to small sample vials and

capped. The fibres were analysed as soon as possible thereafter.

2.4 FTIR-ATR Spectroscopy

Hair fibre spectra were recorded on a NEXUS 870 FT-IR E.S.P Spectrometer fitted with

a SMART ENDURANCETM

Thermo Nicolet Diamond-ATR Smart Accessory (Figure

2.2).

Figure 2.2 - A photograph of the NEXUS 870 FT-IR E.S.P Spectrometer fitted with a

Diamond-ATR Smart Accessory. The arrows indicate the positions of the pressure

tower and the diamond crystal.

Pressure Tower

Diamond Crystal

69

The parameters of the FTIR-ATR analysis were as follows (Table 2.1):

Table 2.1 Specifications and Operating Parameters for the FTIR –ATR Analysis

Number of Co-added Scans 256 Scans

Resolution (cm-1

) 8.0 cm-1

Detector DTGS

Aperture 100 m

Mirror Velocity (cm/s) 0.6329 cm/s

Gain 8.00

Beamsplitter KBr

Internal Reflection Element (IRE) Diamond

A background spectrum was recorded before collection of a spectrum from a fibre. For

the spectral sampling process, the fibre was laid across the face of the diamond crystal

and using the pressure tower, the fibre was compressed to ensure good contact between

the fibre and the crystal. Once a spectrum had been recorded, it was collected and

saved on the OMNIC E.S.P 5.2a Spectral Software Program (as .SPC files). Each

spectrum was ATR corrected using the correction function which is built into the

program to compensate for wavelength dependence (Section 1.5.4).

2.5 Spectral Processing

The OMNIC spectral (.SPC) files were imported into the spectral software package

GRAMS/32AT (6.00, Galactic Industries Corporation, Salem, NH, U.S.A.) as GRAMS

SPECTRAL (.SPA) files for spectral data processing. Firstly the spectra were baseline

corrected and offset to zero. Secondly the spectra were truncated (cut or condensed) in

the 1759-785 cm-1

range which contained the major characteristic -keratin absorption

70

bands. Using the Macro option in GRAMS, the spectral information was sampled and

truncated to one data point every four wavenumbers (254 data points in total) and

transferred to a Microsoft®Excel 2007 spreadsheet and saved (as an .XLS file).

In general, to facilitate spectral comparison, the spectra were normalised to the δ(CH2)

deformation bend (ca. 1450 cm-1

) as an internal standard. The justification behind this

is that this particular molecular fragment is associated with the amino acid side chains

and thus not affected by the peptide backbone conformation changes as a result of

cosmetic chemical treatment from e.g. peroxides or thioglycolic acid or natural

weathering processes.184

235 236

This raw data matrix was then pre-processed by the application of double mean centring

and standardisation in preparation for Chemometrics and PCA.

2.5.1 Derivative Spectroscopy

For the derivative analysis FT-IR spectra, the raw spectra were imported into

GRAMS/32AT (6.00, Galactic Industries Corporation, Salem, NH, U.S.A.) baseline

corrected and truncated (Section 2.5). The final step involved converting the raw

spectra into second derivative spectra using the Savitzky-Golay method. The second

derivative was calculated using a 2o polynomial and a 5-point smoothing function. The

spectra were then reduced to one data point in every four wavenumbers giving 254 data

points in total. These spectra were transferred to a Microsoft®Excel 2007 spreadsheet

and saved as an .XLS file.

2.6 Pre-processing of the Raw Data Matrix and Chemometric Analysis

Data pre-processing is defined as “the use of any mathematical manipulation of the data

prior to the primary analysis”.237

It is utilised to eliminate or reduce irrelevant sources

of variation (either random or systematic errors) for which the primary modelling tool

may not account.

71

2.6.1 Variance Scaling

Scaling of data is used because the treatment concerns both the measurement unit of the

values and the origin of the scale.238

In addition, scaling can be applied to variables or

objects or both. Scaling has to be considered to include:

(1) Shift of the origin of the Cartesian system,

(2) Expansion or contraction of the axes.

2.6.1.1 Double Centring

Double mean centring of a variable is accomplished by subtracting the mean of each

row x, from each element in the row, this is known as x-mean centring. Also, the mean

of each column, y, is subtracted from every element in the column; this is classified as

y-mean centring. This procedure reduces the effect of the variance component reflected

by PC1 of the un-pretreated data set and removes common spectral features.170 172

The

process is described by Equation 2.1 and Equation 2.2238

:

yim = xim – x.m Equation 2.1

followed by;

zim = yim - yi Equation 2.2

where;

yim = column centred datum

xim = datum in row I and column m before centring

x.m = mean of column m = Ixi

im /

zim = double centred datum

72

2.6.1.2 Standardisation

Weighting is performed on the variables to reduce or enhance the variables that

influence the data analysis.237

When the variance of the variables used in the analysis,

differs greatly in absolute size, systematic variation is often masked by the much larger

absolute variance of the major variables. Several methods have been proposed for

selecting the weight factors.237

Sirius includes six different options for weighting of the

subset. One is to equalise the variance of each variable.237

Standardisation is achieved by dividing each element in a given column by the standard

deviation of that particular column for that variable. Thus, every variable has variance

equal to one after this weighting. The primary purpose of this method is to remove the

weighting that is artificially imposed by the scale of the variables.237

This technique is

useful because many data analysis tools place more influence on variables with larger

ranges. The process is described by Equation 2.3 and Equation 2.4238

:

yim = xim/sm Equation 2.3

where;

sm =

2/12

.

1

)(

I

xxi

mim

Equation 2.4

= the estimate of the standard deviation of the variable, xm, about its

mean.

Albano et al. and Derde et al. state that “standardisation of each subset separately gives

a much better resolution in latent variable modelling of subsets”.239 240

2.6.1.3 Autoscaling

Autoscaling is the combination of column centring and standardisation i.e. the use of the

t- transform (studentised variables). The process is described by Equation 2.5238

:

zim = (yim - yi) / sm Equation 2.5

73

2.6.2 Chemometric Analysis

The double centred matrices were imported into the commercially available software

package for multivariate analysis and experimental design, SIRIUS version 7.0 (©

Copyright, Pattern Recognition Systems AS, Bergen, Norway, 1987-1998). These

matrices were then processed to produce the resultant PCA scores-scores plots, loadings

plots and fuzzy clustering tables.

2.6.3 Multi-criteria Decision Making (MCDM)

The multivariate ranking analysis methods, PROMETHEE and GAIA, rank order the

objects according to the modelling of each variable of the matrix and explore the

relationships between objects and variables respectively. The matrix data was imported

into the commercially available Decision Lab software (Decision Lab 2000, Executive

Edition, Visual Decision Inc. © 1999-2003) package for processing.

2.7 Chemometrics

In most fields of chemistry and biology in the 1950s, the processes requiring

investigation had become increasingly complex because acquisition of data was a

severely limiting step.241 242

What had resulted was an abundance of measured data that

required reduction, display and extraction of the relevant information.243

In parallel, the development of computer science and technology allowed chemists to

apply computers combined with advanced statistical and mathematical methods for data

treatment and data interpretation. This eventually led to the formation of a new

chemical discipline, called Chemometrics.243

The term „chemometrics‟ was first coined in 1972 by the Swedish physical organic

chemist Svante Wold of the University of Umea in a grant proposal.244

Kowalski

broadly defined chemometrics as “the application of mathematical and statistical

methods to chemistry”.29 245

Frank et al. expanded on this definition to state

74

“chemometric tools are vehicles that aid chemists to move more efficiently on the path

from measurements to information to knowledge”.246

The more recent definition28

243

describes chemometrics as “the chemical discipline

that uses mathematical, statistical, and other methods employing formal logic;

(a) to design or select optimal measurement procedures and experiments

(b) to provide maximum relevant chemical information by analysing chemical

data and

(c) to obtain knowledge about chemical systems”.

Chemometrics is utilised in numerous disciplines such as statistics, mathematics,

computing, engineering, nutritional science, biology and particularly across all fields of

chemistry.247

In chemistry, the major focus or drive of chemometrics has been towards

solving numerous problems in analytical chemistry fields.241

247

This includes areas

such as industrial chemistry and quality assurance, environmental science, and more

importantly forensic science.247

2.7.1 Chemometrics and Forensic Science

Forensic science is a discipline that formulates conclusions on a purely objective basis.

For example conclusions expressed or presented before a judge and jury pertaining to

the analytical data/results should not show bias or favouritism to the parties involved in

a criminal investigation. Thanasoulias et al. stressed that it is mandatory for forensic

scientists to follow strict, rigid statistical protocols in reaching decisions regarding

analytical data.160

The amalgamation of chemometrics with forensic science is

therefore an important one, as it allows forensic chemists to access complex methods of

analysis capable of generating multidimensional data.161

With chemometric tools

available, efficient extraction of the information is possible, and this allows the forensic

conclusions to be made on information, which is in agreement with forensic protocol.160

161 The advantage of coupling or uniting these disciplines lies in the fundamental

objectives of forensic science (i.e. qualitative analysis such as identification,

75

matching/comparison (PCA and Loadings plots), discrimination and classification,

SIMCA and FC)) being based on chemometric methods/techniques.

The comparison or association of crime scene evidence with known samples from the

suspect can be achieved with pattern recognition methods such as PCA. Furthermore,

once the groups have been identified, the evidence can be strengthened with

classification methods such as SIMCA, and FC and then rank ordered using MCDM.

2.7.2 Principal Component Analysis (PCA)

The human eye is very good at perceiving similarities and differences between objects

of different shapes.248

In chemometrics, the identification of the relationships among

chemically characterised objects is important.242

Effective discrimination and

identification of the objects can be achieved with the aid of exploratory PCA, which is a

well-known pattern recognition method for multivariate data analysis problems.170

PCA

is a data reduction technique whereby the information is arranged into a data matrix

with the selected variables defining the columns and rows (i.e. objects) designating the

sample measurements (e.g. spectra, chromatograms, voltammograms).244

The information is compressed by transforming the data into Principal Components

(PCs), which are orthogonal to one another, with the use of linear combinations of the

original variables (Equation 2.6).

PCjk = ajlxkl + aj2xk2 + …ajnxkn Equation 2.6

where;

PCjk = value for principal component j for object k (the score value

for object j on component k)

aj1 = value of variable 1 on object k

xk1 = measurement for variable 1 on component j

n = total number of the original variables

76

PCs are computed in such a way that PC1 accounts for the largest amount of data

variance, PC2 describes the next largest amount, and the following factors explain less

and less data variance which gradually fade into noise. Thus, much of the data is

accounted for in the first few PCs. Information loss is virtually ruled out by this method

of data reduction.180

249

As each object (sample) has a value (score) on each PC, PCA plots (or scores plots)

provide a convenient means of displaying the data diagrammatically. This allows for

subsequent investigations of relationships (clustering) and discrimination (separation)

between the objects. Further information or evidence can be obtained from PCA plots

by highlighting which variables have significant weighting on a PC (positive or

negative), and also, indicating which objects are strongly related to those variables.

This information is possible through the analysis of loadings (weights) plots for each

PC, where the values of the „ajn‟ coefficients in equation 2.6 are plotted against

variables such as wavenumbers, time and voltage. High positive or negative values

reflect the importance of those variables for that PC, whilst low loadings indicate that

those variables are insignificant to that PC.

2.7.3 Classification

Classification of samples is one of the principal goals of pattern recognition.244

For the

analyst, the objects to be classified can be samples for which chemical analysis of their

constituents are obtained or the spectral data measured for a compound. Methods for

classification can be divided into supervised (Soft Independent Modelling of Class

Analogy SIMCA) approaches and unsupervised (Fuzzy Clustering FC).244

2.7.3.1 Soft Independent Modelling of Class Analogy (SIMCA)

For the supervised method, a test (training) set of objects is required where the samples

origins are known which quantitatively establish the basis on which those objects were

classified, allowing objects of unknown class to be sorted.242 244

The most commonly

used method of modelling is the SIMCA (soft independent modelling of class

analogies) approach.245 250

In SIMCA, PCA is used to develop a model of each group or

class within the training set. The members of such a set are selected by the user. The

77

number of statistically significant PCs that describe each class are determined by cross-

validation.244 251

The data for each object in a class are partitioned into information that

is explained by the class model and into residuals which describe the non-systematic

variance.170

A model can be expressed by the following equation252 253

:

Xki = Xi +

p

ij

+ ajiujk + eki Equation 2.7

where;

p = is the number of the principal components in the class model

eki = is the residual value of object k on variable i

Residual standard deviations (RSD) are computed for a class as a whole and for each

object. The former measures the mean distance between the objects of a class and the

class model; the latter measures the orthogonal distance between the object and the class

model.170

This RSD indicates how well the object is explained by the class and is

calculated using the following equation253

:

RSD[c] =

2/1

)1/)( 2][

1

PNeccx

cxi

i

Equation 2.8

where;

ex[c] = error of object x fitted to class model C

Nc = number of objects of class C

P = number of principal components

RSD[c] = Residual Standard Deviation of class C

78

Assuming the residuals to be normally distributed, a critical F ratio for a selected level

of significance can be computed which in turn will yield a critical distance (RSDcrit) that

defines the class boundries.170

The distances between different sets of classes can also

be established by selecting one class as the model set. The model set is chosen on the

basis that the class contains a substantial number of objects.22

This is essential because

SIMCA is a parametric method and is influenced by the number of samples in a class.

Small sample sizes do not reflect the results of the true population253

, and thus

subsequently the significance of the results is questionable.

Once class models are established, further information can be obtained. The modelling

power of each variable for each class gives the analyst an indication as to how

significant the variable is for a given class model based on the distance values. Values

of less than one indicate a very small degree of difference, while values greater than

three signify that the two classes are quite different.253

Whereas PCA generally may display information in 2 or 3 dimensional space, SIMCA

class models may include any number of statistically significant PCs. A completely

different method to data classification is the unsupervised approach, an example of

which is the Fuzzy Clustering method.254

2.7.3.2 Fuzzy Clustering (FC)

Fuzzy clustering (FC) is a non-hierarchical cluster method; i.e. clusters are not formed

either by merging small groupings into larger ones or, conversely, by subdividing large

clusters.255

The FC method is a non-parametric method and is well described by

Adams.256

The aim of FC is to highlight similar objects as well as to provide

information regarding the relationship of each object to each cluster.256

In conventional classification a given object is considered to have unique membership

of a class; i.e. its membership of any other class is zero. Alternatively, the FC approach

attempts to assign a degree of class membership for a given object over a number of

classes.241 256

79

Classification is performed with the aid of a membership function which may be

specified, for example170

:

m(x) = 1 – c|x – a|p Equation 2.9

where;

a = constant

c = constant

p = positive exponent

The classification could also be constructed on the basis of the data of interest. Thus, a

membership value for each class is assigned for each object. In the SIRIUS software,

the degree of fuzziness can be varied by a weighting exponent value between 1.0 to 3.0.

The sum of the membership values for each object is between 0-1. The benefit of FC is

that it facilitates the discrimination between objects that markedly belong to one cluster,

i.e. values close to 1 yielding hard (unique) membership; and objects that are members

of several clusters, i.e. a membership value of 1/No. of clusters (fuzzy membership).

As the Forensic scientist must be impartial to the analysis of any collected evidence

(Section 2.7.1), this investigation has chosen FC so that the classifications of the spectra

are un-biased.

2.7.4 Multi-criteria Decision Making Techniques (MCDM)

As human beings, we are faced with making decisions all the time. In 2002, Brans

suggested that humans (in the context of the real world) naturally use a decision making

approach, which is based on measurement, estimation and modelling. These models are

usually approximations of reality.257

The decision making process is based on three

elements: rationality, subjectivity and ethics.258

Out of this philosophy developed a

non-parametric multi-criteria decision making method (MCDM) which is based on the

ranking of objects.

80

MCDM is a multivariate data analysis technique that is principally concerned with the

optimisation, selection and decision making of the response to a given procedure.258

The response is the criterion by which the procedure is evaluated, i.e. the optimisation

criterion. Problems are solved by modelling the response as a function of the variables

that influence that criterion after carrying out an experimental design.258

This technique

permits large volumes of data to be processed, allowing the analyst to explore and

understand the relationships between different parameters.259

For example, MCDM methods are broadly applied today to a multitude of problems,

e.g., the comparison of baseball teams, development of negotiation support systems,

selecting landmine detection strategies, etc.258

Also, many applications of MCDM can

be found in scientific fields such as the environment, agriculture, civil engineering and

medicine.

MCDM methods commonly offer partial pre-ordering as well as net full ordering or

ranking of objects. In full ordering, the objects can be ordered either top-down or

bottom-up depending on the index value (designated Φ+

or Φ-). Top-down or

maximised ranking, the largest index value is preferred whereas bottom-up or

minimised ranking the smallest index is preferred.258

Partial pre-ordering is concerned

with the situation where objects may perform equally well but on different variables in

that they cannot be compared and one object cannot be preferred to others.

Many MCDM methods exist for the handling of multi-variate situations. Preference

Ranking Organisation Method for Enrichment Evaluation (PROMETHEE) is one of the

better performing methods which is well established and is the technique that has been

used in this work.258

2.7.4.1 PROMETHEE I and II Multivariate Techniques

PROMETHEE is a non-parametric method applied in Euclidian space to rank objects.258

In PROMETHEE, each variable in the raw data matrix are set to maximise or minimise.

It is then converted to a difference, d, matrix achieved by comparing all values pair wise

by subtraction in all possible combinations.

81

The user then selects a so-called preference function for each criterion. A preference

function P (a, b) defines how much outcome a has to be preferred to outcome b. If the

values of the defined preference are between 0 and 1, then P = 0.1 is a weak preference

whereas P = 0.9 is a strong preference. The degree of preference is expressed on a

percentage scale. In practice, this preference function is a function of the difference, d,

between the two evaluations260

:

P(a, b) = P(f(a) – f(b)) Equation 2.10

A graph of the function is presented in Figure 2.3. It is a non-decreasing function, equal

to zero for negative values of d = f(a) – f(b).

Figure 2.3 – A preference function P(d).260

In general, one may consider a function H(d) which is directly related to the preference

function, P260

:

H(d) = {P (a, b), d ≥ 0

{P (a, b), d ≤ 0 Equation 2.11

1

0 d

P(d)

82

This function is then represented in Figure 2.4.

Figure 2.4 – Function H(d).260

The preference indices are then computed for each d value for each object with the use

of one of six mathematical functions (Decision Lab 2000, Executive Ed., Visual

Decision Inc., © 1999-2003), selected independently for each variable. The analyst can

improve the quality and the reliability of the decision-making processes because of the

structured procedure and the visual analytical aids. The information requested from the

analyst is limited to a number of key parameters that can be precisely fixed, ensuring

high quality results.261

Furthermore, the software allows the decision maker to directly use the data of the

problem in a simple multi-criteria table.

Preference of b over a Preference of a over b

H(d)

1

d 0

83

The six types of preferences available are (1) Usual, (2) U-shape, (3) V-shape, (4)

Level, (5) Linear and (6) Gaussian.260

The choice of the preference functions is crucial

because they define how much one location has to be preferred to other locations.258

(1) Usual Criterion:

For this preference function, there is a difference between a and b if f(a) = f(b); as soon

as the two evaluations are different, the decision maker has a strict preference for the

action having the greatest evaluation. For this preference function, no parameter has to

be defined.260

(2) U-Shape or Quasi-criterion

For this preference function, the two actions are indifferent to the decision maker as

long as the difference between their evaluations, i.e. d, does not exceed the indifference

q. For the U-shape preference to be utilised, the decision maker-must determine the

value of q that is the greatest value of the difference between two evaluations that the

decision maker considers indifferent.260

(3) V-Shape Criterion

For this preference function, if d is lower than p, the preference of the decision maker

increases linearly with d.260

However, if d becomes greater than p, a strict preference

situation is created known as the V-shape function. When the V-shape criterion is

chosen, the decision maker has to determine the lowest value of d above which they

consider there is strict preference of one of the corresponding actions.260

(4) Level Criterion

For this preference function, an indifference threshold q and a preference threshold p are

simultaneously defined. If d lies between q and p, there is a weak preference situation

(H(d) = ½). The decision maker has two thresholds to define.260

84

(5) Linear Criterion

In this scenario, the decision maker considers that the preference increases linearly from

indifference to strict preference in the area between the two thresholds q and p. Two

parameters are to be defined.260

(6) Gaussian Criterion

The Gaussian preference function requires the determination of the standard deviation,

σ, which is made according to the experience obtained with the normal distribution in

statistics. As this function has no discontinuity it provides stability to the results260

This refers to the influence of the thresholds on the rankings. Brans et al.260

state that

“the results given by Gaussian criteria, with very „smooth‟ preference functions are still

better”.

The six preference functions available in Decision Lab 2000, including the shape of the

graphs and the mathematical justifications for each preference function are summarised

in Table 2.2.

85

Table 2.2 List of Preference Functions

Preference Function

(Decision Lab 2000,

Executive Ed., Visual

Decision Inc. 2003)

Shape262

Mathematical

Justification260

Usual (no threshold)

H(d) = 0 {d=0

H(d) = 1 {d≠0

U-shape (q threshold)*

H(d) = 0 {-q ≤ d ≤ q

H(d) = 1 {d < -q or d > q

V-shape (p threshold)†

H(d) = d/p {-p ≤ d ≤ p

H(d) = 1{d<-p or d > p

Level (q and p thresholds)

H(d) = 0} [d] ≤ q

H(d) =1/2} q<[d]≤p

H(d) = 1} p < [d]

Linear (q and p thresholds)

H(d) = 0} [d] ≤ q

H(d) = ([d]–q)/(p-q)}q<[d]≤p

H(d) = 1} p< [d]

Gaussian (σ threshold)‡

H(d) = 1-exp{-d2/2σ

2}

NB: (*) = Indifference threshold, q, which represents the largest deviation that is

considered negligible by the decision-maker.

(†) = Preference threshold, p, represents the smallest deviation that is considered as

decisive by the decision-maker. p cannot be smaller than q.

(‡) = Gaussian threshold, σ, is the standard deviation

86

The next step involves calculating a preference index Π (a, b) of experiment (a) over

experiment (b) for all criteria in the equation258

:

Π (a, b) =

k

j

jj baPw1

),(* Equation 2.12

where;

k

j

jw1

1 Equation 2.13

where;

k = is the number of criteria

wj = is the weight for each criterion

The values of Π (a, b) are between 0 and 1 and illustrate the global preference of (a)

over (b).

From the individual preference indices the overall indices are computed for each object

giving the positive Φ+ and negative Φ

- flows. The positive flows are the best

performing and expresses how each experiment outranks all the other experiments. The

negative flows are the least performing objects and states how each experiment is

outranked by all the other experiments. The higher Φ+ and the lower Φ

- the better

experiment.258

87

The Φ+ and the Φ

- outranking flows are calculated as follows:

Ψ+ (a) =

Ax

xa ),( Equation 2.14

and;

Ψ- (a) =

Ax

ax ),( Equation 2.15

PROMETHEE consists of pair wise comparisons of all the experimental results and

leads to a partial ranking pre-order of the objects according to three rules258

:

1. a outranks b if:

Ψ+ (a) > Ψ

+ (b) and Ψ

- (a) < Ψ

- (b) Equation 2.16

or

Ψ+ (a) > Ψ

+ (b) and Ψ

- (a) = Ψ

- (b) Equation 2.17

or

Ψ+ (a) = Ψ

+ (b) and Ψ

- (a) < Ψ

- (b) Equation 2.18

2. a is indifferent to b if:

Ψ+ (a) = Ψ

+ (b) and Ψ

- (a) < Ψ

- (b) Equation 2.19

3. a cannot be compared with b

in all other cases where b does not outrank a

(using a weighted sum of the two criteria)

If experiment a is every good on one criterion where experiment b is weak and,

reciprocally, b is good on the other criterion where a is weak, then the two experiments

cannot be compared because they are too different.

88

In PROMETHEE two types of ranking are possible:

1. PROMETHEE I - is partial ranking where objects a and b cannot be

compared with one another (i.e. rule 3 included)

However, to establish a complete rank order, the user can calculate the PROMETHEE

II, net outranking flow, Φ, by258

:

Φ (a) = Φ+ (a) – Φ

- (a) Equation 2.20

2. PROMETHEE II ranking eliminates the incomparability rule and

therefore appears to be more efficient. However, it is less reliable than

the results derived from PROMETHEE I

An outranking flow graph can be drawn for both the partial and complete pre-order to

visualise the information, and to support the decision maker. However, when large

matrices are used the PROMETHEE I diagrams become very complex and challenging

to interpret, and PROMETHEE II net flows are preferred.

2.7.4.2 GAIA

Since the assignment of weights to the different criteria is an important option for

MCDM methods, a sensitivity analysis is a useful tool.258

The easiest way to achieve

this for PROMETHEE is to apply GAIA (Geometrical Analysis for Interactive Aid).

GAIA is a visualisation technique that complements the PROMETHEE ranking by

providing guidance for the importance of the principal criteria.263

GAIA essentially

provides a PC1 versus PC2 bi-plot, the matrix for which is generated by decomposing

the net outranking flows Φ (a).258

The GAIA plane offers a visual representation of the data, with some clearly defined

symbols.261

Criteria (or grouped categories of criteria) are represented by axes. On the

GAIA plot, the longer a projected vector for a criterion, the more variance it explains.

A criterion vector highlights the differences and similarities of the objects. If the

89

criteria vectors are oriented in the same direction, they are correlated; the preferences

are similar. Independent criteria are characterised by almost orthogonal vectors and

conflicting criteria have vectors in opposite directions.258

The objects or samples that

are projected in the direction of a particular criterion vector are strongly related. Similar

objects are therefore visualised as a cluster and dissimilar objects will be located in

other directions.

The weight or decision vector, Π, is composed of the weights, normalised to one, of the

different criteria. It is the weighted mean of the vectors of the different criteria.258

The

projections on that vector follow the order of complete PROMETHEE net ranking. If

the decision vector is short, the criteria are in conflict; where the decision vector is

nearly orthogonal to the principal components plane and the decision power of the axis

is therefore weak. However, if the vector is long, the most significant criteria are

highlighted in that direction and as far from the origin as possible.258

Hence, the

decision power of the axis is strong. A 3D-representation of the Π decision axis

emphasises the position of the axis.

Although GAIA gives the best possible 2D-representation of the data, usually some

information gets lost in the process. To control the quality of the GAIA plane, the Δ

value is always displayed in the GAIA planes window, measuring the amount of

information preserved in the GAIA plane.262

In practice, Δ values larger than 70 %

correspond to reliable GAIA planes; Δ values lower than 60 % should be considered

with care.262

This dissertation now advances towards the analysis of the morphological and structural

properties of human hair keratin via SEM and FTIR-ATR Spectroscopy.

90

3.0 CUTICLE SURFACE TOPOGRAPHY AND FTIR-ATR

SPECTRAL CHARACTERISTICS OF THE MORPHOLOGICAL-

CHEMICAL STRUCTURE OF HUMAN HAIR FIBRES

Across the major scientific fields, biological human hair fibres have been studied for a

number of key purposes, i.e. medical264

, environmental264

, cosmetic51 52 265

and more

importantly, forensic science.24-27 47 158 266 267

In forensic science, fibre evidence is

useful for matching fibres from a crime scene directly with known fibres from the

alleged suspect or victim with the use of quality-assured comparative methods.

Structural elucidation techniques exist such as FT-IR spectroscopy, which facilitate the

matching of the chemical structure of identified and questioned fibres. In general, FT-

IR spectroscopy is a popular technique, chosen for its sensitivity to the conformation

and local molecular environment of molecules in biopolymers.268

In the judicial field, it

has been suggested by Robertson that “infrared spectroscopy is a powerful technique for

the forensic examination of fibres”.3

Many FT-IR spectral sampling techniques are available for the study of structural

chemistry of α-keratin hair fibres. However, some exhibit substantially better spectral

resolution, and are able to yield more substantial chemical information. From the

forensic perspective, the selection of an appropriate IR technique is critical.

The chosen technique should facilitate the extraction of reliable information from the

fibre so as to obtain clear outcomes. Over the past few years there has been much

debate and discussion as to what the optimum IR sampling technique for hair is. From

the forensic perspective it is incumbent upon a forensic scientist to use tests that carry

the highest discrimination power and be aware of (and express) the limitations in the

technique.

Research at Q.U.T., Brisbane, Australia,22 23 25-27

over the past decade has endeavoured

to improve the understanding of such complexities, and thus far the results have

suggested a useful approach involves the utilisation of FTIR-ATR spectroscopy in

91

conjunction with chemometric methods for interpretation.23

FTIR-ATR spectroscopy

produces spectra which are apparently clear of “peak saturation” or “band saturation”

observed in the spectra of competing techniques.51 181 182

This observation has been well

supported by Kirkbride, Robertson and Royds, from the Australian Federal Police

force.3 187 269

Although the quality of the spectra has been appreciably improved with the amendment

of the sampling technique, the vibrational spectrum of human hair keratin itself,

particularly within the wavenumber range of 1750-800 cm-1

, is very complex. This is a

result of the chemistry of the protein-polypeptide structure of hair keratin. Three

specific groups within the keratin protein give rise to different vibrational absorption

bands that can be observed within this fingerprint section. They are:

(a) The peptide bond (primary protein structure). Formed by a condensation

reaction between the carboxylic acid and amine group of adjacent amino acids.

It is the most abundant within the keratin protein and yields the Amide I, II and

III IR spectral bands.

(b) The polypeptide chain (secondary protein structure). Pertains to the C-C

skeletal backbone of all keratin proteins and can exhibit one to three

conformationally sensitive patterns, those being the α-helical, β-sheet and

random coil or amorphous structures directly related to the Amide bands; and

finally,

(c) The amino acid side chains (R groups). The C-H vibrations originating

from the -CH, -CH2 and -CH3 of the aliphatic and aromatic rings of

phenylalanine, tyrosine, tryptophan and the significant vibrations of the

oxidative intermediates from the amino acid cystine (i.e. S=O, SO2, SO3-, and -

S-SO3-).

Special mention should also be made of water, which is an integral part of the keratin

supermolecular structure.270

Water affects both the amorphous and crystalline phases of

keratin.

92

Keratin‟s high affinity for water is evident over the whole range of relative humidities,

particularly within 65% RH to 95% RH. Under conditions of low temperature or short

times for which no structural mobility can occur in an α-Keratin fibre, the mechanical

properties of the fibre will depend primarily on the whole cohesive bond network.10

Although water vapour permeates the hair readily, there is some binding selectivity

within the molecular structure and accessibility restraints in the filament and matrix

texture.32

It has also been recognized that the nature of the structural chemistry can affect the %

moisture content, which essentially refers to weathered and cosmetically treated hair

fibres. Also, it has been well established that chemical disruption of the fibre

contributes to increased swelling at moderate-to-high humidities.32

Water is a polar molecule and two types of water are associated with the α-Keratin

protein, absorbed or „bound‟ water and adsorbed or „free‟ water. At low humidities,

water molecules are principally bonded to hydrophilic side chains (guanidine, amino,

carboxyl, phenolic, etc.) and peptide bonds through hydrogen bonds and Coulombic

interactions. At higher humidities, water enters as „solution water‟ not attached to

specific sites but with absorption resulting from the free energy difference arising from

the entropy of mixing keratin with water.11

At very high % RHs (>80%), multi-

molecular sorption (water-on-water) occurs, and this refers to the „free‟ water

interacting and condensing onto the first „bound‟ layer.10 11

The thermal transitions of keratin have been discussed in many journals devoted to the

properties of wool, horn or human hair fibres.270

In 1960, Schwenker et al. were one of

the first groups to investigate the thermal properties of various keratin fibres by DTA

under a nitrogen atmosphere. It can be concluded that as a hair fibre is heated, it goes

through a number of changes/phases before its eventual degradation to charred residue.

Between 80-140oC is the endothermic removal/evaporation of loosely and strongly

bound water from the hair fibre. The main peak at approximately 110oC represents the

loss of adsorbed water, whilst the shoulder peak at roughly 160oC refers to the

endothermic loss of strongly bound water from the hydrophilic sites in the fibre.271

93

Therefore, as water plays a fundamental role in the overall mechanical strength of the

hair fibre, it is reasonable to suggest that the –OH bands of absorbed and adsorbed

water will be present in the IR spectrum of α-Keratin.

As a consequence of the dominance of the strong peptide bond vibrations, significant

structural information (approximately 50% of the total absorptions) relating to the

keratin protein remains concealed in the IR spectrum that has the potential to be utilised

for identification and discrimination of human hair fibres, particularly for forensic

purposes.

However, application of Derivative Spectroscopy can facilitate the unravelling and

unveiling of the overlapped absorption bands. For this work, derivative analysis on raw

spectra is a novel approach to extricating the convolution or complexity of the hair

keratin spectrum.

This chapter critically examines and compares the complexity of various human

hair FTIR-ATR spectra. The hair has been collected from many individuals of

different genders and human races i.e. Caucasian, Asian, and African-type. To

support the conclusions of the spectral examinations, a brief morphological

analysis of the cuticle surface topography of typical hair fibre types was conducted

with the use of SEM.

The proposed forensic protocol (Section 1.5.3.2) for analysing human hair

evaluates the fibres in a systematic approach. The spectral comparisons and

subsequent band assignments will involve a) contrasting the general raw or non-

chemically treated fibres with cosmetically treated hair fibres which have been

subject to differing levels of treatment, b) analysing mean difference spectra

between gender and each race, and finally, c) an investigation of second derivative

FTIR-ATR spectra of typical untreated and treated fibres.

94

3.1 Morphological Characteristics of the Cuticle Surface Topography

of Human Hair Fibres Involving SEM

3.1.1 Comparison of Chemically Untreated and Cosmetically Treated Human Hair

Fibres

In order to discern and identify the impact that various chemical treatments have on a

human hair fibre, one must first understand the character of a fibre in its natural,

untreated state.

Initially, in general, the term „non-treated‟ or „untreated‟ hair is strictly defined in this

context as hair fibres that have not undergone any form of intentional cosmetic chemical

treatment such as bleaching, permanent waving, straightening and permanent dyeing

that results in causing oxidative damage to the fibre. The definition of cosmetic

chemical treatment does not normally extend itself to the utilisation of shampoos and

conditioners, because these products are essential daily requirements that assist in the

hygienic maintenance of the hair and scalp, rendering it free of sebaceous oils, dirt and

soils and dandruff.

However, past SEM studies have also indicated that the mechanical processes such as

brushing, towel drying, weathering by exposure to rain, and dirt as well as the chemical

damage from UV radiation all result in physical damage to the surface architecture of

human hair fibres.48 65 67 72

272

The damage manifests itself as the jagged-like edges of

the cuticle scales, sometimes causing them to lift and become completely removed from

the surface, exposing the underlying cortical layers. With the protective external layer

removed in some places, the damage renders the fibre more susceptible to further

chemical degradation from natural chemical weathering, such as sunlight, salt and

chlorinated water.

Hence, as these processes occur in normal every-day life for the majority of individuals

in developed countries, the term „untreated hair fibre‟ is still adequate when used in this

95

context. However, the differences between untreated and physically treated fibres

will be investigated further through the IR spectral and chemometric analyses.

3.1.1.1 SEM Analysis of Non-Treated Hair Fibres

SEM micrographs were obtained from three typical untreated hair samples from both

genders. An SEM micrograph of a 53 year old Asian female (Asian female No.17 in

Appendix I) is displayed in Figure 3.1. The fibre is approximately 80 µm in diameter

with the edges of each cuticle scale roughly 10 µm apart longitudinally.

Figure 3.1 – SEM image of an untreated Asian female hair fibre.

The external cuticle layer image shows each of the individual scales at high resolutions.

Each cuticle scale is uniquely shaped - some have smooth rounded edges and others

with jagged-like edges, overlapping each other as they ascend along the length of the

fibre towards the tip. Overall, the fibre is structurally undamaged with very minimal

cracking towards the centre of the image. Small (less than 1 m in size) pieces of

debris or soil particles, represented by the white specs, adhere randomly to the fibre;

nonetheless the fibre appears to be relatively clean. It is reasonable to suggest that some

10 µm

Cuticle Scale

96

dirt and debris or other foreign particles would be associated with the hair through

normal everyday processes.

The SEM micrograph in Figure 3.2 is of a 23 year old Caucasian male (Caucasian male

No. 4, Appendix I). The hair fibre is approximately 60 µm in diameter and the cuticle

scales are spaced approximately 18 µm apart. There is no evidence of any damage or

debris on the surface of the fibre as the cuticle scales are relatively smooth and spaced

neatly apart.

Figure 3.2 – SEM image of an untreated Caucasian male hair fibre.

The final untreated fibre is of a 22 year old African male (African-type male No. 8,

Appendix I; Figure 3.3). The fibre is approximately 70 µm in diameter and the cuticle

scales are spaced approximately 8µm apart longitudinally. The surface appears to be

covered by many cuticle scales compared to the fibres depicted in Figures 3.1 and 3.2.

Some of the cuticle scales in-fact are jagged-like in appearance, however the fibre itself

appeared relatively clean due to the lack of debris.

Cuticle Scale

18 µm

97

Figure 3.3 – SEM image of an untreated African hair fibre.

Hence, in summary, these three fibres illustrate typical untreated hair fibres

sampled directly from the scalp at any given time.

3.1.1.2 SEM Analysis of Different Cosmetically Treated Hair Fibres

SEM images were acquired from a number of hair fibres that had undergone different

forms of cosmetic chemical treatment ranging from the gentle external cosmetics such

as moisturisers and gels, to the harsh oxidative treatments such as permanent dyeing,

bleaching and waving.

The majority of the African hair samples originated from the United States of America,

Nigeria and Sudan. It was immediately apparent that a number of chemical treatments

had been applied to the hair fibres such as perming, straightening and dyeing as well as

the use of surface treatments such as moisturisers. Relatively few of the samples were

completely free of chemical treatment according to analysis of the hair histories of these

individuals. African-type hair fibres characteristically have more crimp, as compared to

the other races. As a result, the hair has a greater tendency to knot (African-type male

No. 6, Appendix I, Figure 3.4), making it often difficult to comb and style.

Cuticle Scale

8 µm

98

Figure 3.4 – SEM image of the tip end of a treated African male hair fibre that has

formed a knot possibly caused by the effects of grooming.

Compatibility tests have been conducted on African-type hair using a tress of hair

attached to a strain gauge, which measures the force required to pull the comb through

the tress. The results have illustrated that the engagement and motion of the comb lead

to a displacement and intensification of individual curl entanglements, as reflected by

the immediate and progressive rise in the combing force.32

However, in wet combing,

the curly geometry of African hair resists fibre adhesion and clumping (as was also

observed with Caucasian hair) with the curls slightly relaxing. This lessens the extent

of individual entanglement. The torsion and bending moduli decrease, facilitating the

unbending of curls and their twist passage between the teeth of the comb.32

Consequently, persons of African origin generally prefer, or are forced to have their hair

straightened, relaxed or permed chemically and physically in order to render it more

manageable, and also to maintain general hygiene of the hair as it is prone to the build-

up of dirt and oils attributed by the geometry.

Knotted Fibre – African Male

99

A cosmetically treated hair fibre SEM micrograph (Figure 3.5) is from an 18 year old

African-American male (African-type male No. 6 in Appendix I; ca. 80 µm in

diameter). The only form of cosmetic treatment claimed to have been used by this

particular individual is the application of a moisturiser known as a “pink lotion”. This

type of moisturiser is a popular product amongst African Americans, or persons of

African origin because it protects the hair from dryness and brittleness as a result of

blow drying, hot curling, or combing. The product is specially formulated to maintain

the hairs natural moisture level.219

Figure 3.5 – SEM image of the same treated African male hair fibre (Figure 3.4) which

has been subject to a “pink” moisturising lotion. This image illustrates lifting and

chipping of the cuticle scales.

In general, the cuticle surface of this fibre is inherently different to the surface

topography of the untreated hair fibres. The edges of the cuticle scales are severely

jagged in appearance with pieces of the cuticle seemingly “chipped away” in most

places. At some locations of the cuticle scale edge, it is difficult to ascertain whether

pieces have been torn off, or debris has adhered to the fibre. Furthermore, white areas

of the cuticle layer, as indicated on the micrograph, are in fact regions where the cuticle

cell has been up-lifted further from the surface, exposing the underlying layer.

“Chipped” Cuticle

“Lifting” Cuticle Layers

100

As the fibre had not been subject to any form of chemical treatment, the micrograph

suggests that the damage caused to the surface could be ascribed to physical or

mechanical processes. This provides supporting evidence that combing or maintenance

of African-type hair is difficult and abrasive.

Figure 3.6 is an SEM image of a hair fibre from a 23 year old Asian female with

permanently dyed hair (ca. 77 µm in diameter). In direct contrast to the untreated

female Asian hair fibre, the surface topography of the fibre appears to be markedly

different. The majority of the cuticle scales of this fibre represent the trademark

“jagged” or chipped” appearance, with the cuticle broken off in random locations along

the length of the fibre. This is attributed to the affects of oxidative permanent dyeing.

Hence, this observation suggests that chemical damage is not uniform along the surface

of the fibre; the damage appears to be random.

Figure 3.6 – SEM image of a permanently dyed Asian female hair fibre.

“Jagged” Cuticle

“Breaking” Cuticle

101

Figure 3.7 shows the external cuticle layer from a randomly sampled fibre from a 53

year old Caucasian female with bleached hair which has been treated with a semi-

permanent dye (ca. 60 µm in diameter). The fibre appears to be unaffected by the

application of the semi-permanent dye. This is to be expected as semi-permanent

dyeing involves no chemical reaction with the chemical structure of the fibre, only a

diffusion of coloured molecules from solution into the hair cortex.31

Figure 3.7 – SEM image of a bleached and semi-permanently dyed Caucasian female

hair fibre that receives constant sun exposure.

The scales are characteristically jagged, yet not chipped, and the surface appears to be

somewhat smoother in relation to permanent dyeing, suggesting that the cuticle has

been removed in certain locations as indicated by the uplifting of the cuticle. The

morphological analyses of each fibre provided information pertaining to the surface

topography of different hair samples. These observations will be corroborated with the

information drawn from the principle technique used in this study, FTIR-ATR

spectroscopy.

“Lifting” Cuticle Layers

“Smoothing”

102

3.2 Structural Elucidation of -Keratin Hair Fibres using FTIR-ATR

Spectroscopy

3.2.1 Comparison of Chemically Untreated and Cosmetically Treated Fibres

3.2.1.1 Secondary Structure Conformations and Vibrational Modes of the Peptide Bond

In keratin, the peptide linkage (i.e. primary protein structure) is quite rigid due to partial

double bond character. This is caused by resonance of electrons between the oxygen

and nitrogen atoms yielding a partial C=N bond.273

The modes of vibrations of the

peptide bond give rise to the characteristic bands known as the Amide I, II and III

bands. Their frequencies are sensitive to peptide conformation and the type of

hydrogen bonding. This sensitivity of the peptide bond affects the secondary protein

structure defined by the local conformation of its polypeptide backbone.274

These local conformations are specified in terms of regular folding patterns known as

helices, pleated sheets or turns, which are established by their X-ray diffraction patterns.

274 275 These illustrate a regular repetition of particular structural units with certain

repeat distances.274

Pauling and Corey demonstrated through X-ray analyses that the

polypeptide chain can interact with itself in two major ways: through conformation of

an α-helix and a β-pleated sheet.274

For the α-helical conformation, the right-handed helix (3.6 amino acid residues per turn

and a repeat distance of 1.5 Å) is favoured. The structure is created through:

a) intra-molecular hydrogen bonding between the carbonyl oxygen of one

peptide bond and the hydrogen atom of another as well as side chain amino and

carboxyl groups

b) hydrogen bonding of water with amide, carboxyl and hydroxyl groups

c) coulombic interactions between the charged side chains of lysine, arginine,

histidine and glutamic and aspartic acid, and

103

d) covalent, disulphide links between different chains or between different parts

of the same chain.

It has also been suggested that if two or three strands of polypeptides are coiled or

spiralled about each other analogous to a twisted rope, the structure is commonly

referred to as the “coiled coil” model.

In contrast, the β-sheet pattern has a characteristic conformation pattern in an extended

form arranged in sheets. This conformation is observed in feather keratin and stretched

mammalian keratin. It relies on inter-chain hydrogen bonding between amide groups of

adjacent chains.276

Small and medium sized R groups have enough room to avoid van

der Waals repulsions. The structure has a longer repeat distance of 7.0 Å compared to

that of the α-helix.274

The keratin peptide chain can also assume what is described as a random coil or

amorphous arrangement. The structure is flexible, changing, and statistically

random.274

Broad vibrational bands present in the spectra of hair fibres can be attributed to the

presence of different types of secondary structure.149

Also, within one type of

secondary structure the dihedral angles of the peptide backbone chain vary over a wide

range.277

As a consequence of band broadening, the relative contributions of the different

conformations are difficult to observe in the raw spectrum, but this will be more

appropriately discussed and interpreted with the aid of derivative spectroscopy (Section

3.3.2).

3.2.1.2 FTIR-ATR Spectral Analysis of Untreated Hair Fibres

A selection of 12 spectra of typical non-treated hair fibres originating from both male

(M) and female (F) donors across the Caucasian (C), and Asian (A) and African-type (N

(Negroid)) races are presented in Figure 3.8.

104

Figure 3.8 - A selection of 12 typical untreated FTIR-ATR spectra of human hair fibres

from male (M) and female (F) donors of the major races: Caucasian (C), Asian (A) and

African-type (N). (Note: The vertical lines designate the vibrational assignment and

peak position of each functional group/molecular fragment. The arrows indicate the

direction of the vibration).

8001000120014001600

Wavenumber (cm-1

)

Ab

sorb

an

ce (

a.u

.)

CF1

CF2

CM3

AM6

AM20

AF18

AF17

CM8

NM1

NF21

NF20

NM2

1627 cm -11520 & 1511 cm

-1

1234 cm-1

1114 cm-1 1071 cm

-1

C=O

1735 cm-1

1445 cm1392 cm

-1

-1

1037 cm-1

COO

1577 cm

-

-1

105

The untreated hair fibre samples were received from individuals who had not performed

any form of cosmetic treatment to their hair which also included the utilisation of

surface applications such as hair gels, waxes, mousses, moisturisers, and did not spend

exceedingly long periods in the sun. This strict sampling was purposefully carried out

in order to ensure the integrity of the band assignments of typical untreated fibres.

Each spectrum has been normalised with the use of the δ(CH2) deformation bend (ca.

1450 cm-1

) as an internal standard. The justification behind this is that this particular

molecular fragment is associated with the amino acid side chains, and thus, not affected

by the peptide backbone conformational changes as a result of cosmetic chemical

treatment with e.g. peroxides or thioglycolic acid or natural weathering processes.184

235,236,278 It has been suggested that the intensity differences of this band from sample to

sample are minimal.184

The untreated spectra will be discussed first, followed by the chemically treated ones, to

illustrate the transformation of the structural chemistry within the keratinous fibre from

the untreated state to the cosmetically treated one.

For the untreated fibre spectra, assigning from the higher wavenumber (cm-1

) region,

the vibrations of the three Amide bands from the peptide bond generally occur at 1700-

1590 cm-1

, 1580-1500 cm-1

, and 1320-1210 cm-1

respectively.24

The first absorption arises from the peptide linkage, and is the Amide I band, which

involves about 80% C=O stretching coupled with an in-plane bending of the N-H and

C-N stretching modes. The band is a broad and strong peak at approximately 1627 cm-1

and remains remarkably consistent between genders and race. This is illustrated by the

lack of shift of each Amide I band across the vertical line. The complexity of the band

is ascribed to either the coupling between two or more similar carbonyl stretching

modes or the heterogeneity among the backbone carbonyl groups.273

Heterogeneity can

occur from fundamental basic differences among carbonyls and/or from

conformationally related differences in the strength of the hydrogen bonds associated

with the carbonyls.273

106

An absorption from two of the amino acid side chains is masked by the strong intensity

of the Amide I vibration. Hair keratin is made up of a composition of the 20 different

amino acids; two of those are classified as carboxylic acid or acidic amino acids;

aspartic and glutamic acid. These acidic side chain residues give rise to different IR

absorptions dependent on the pH of their environment.184

At low pH values the carboxylic acid groups would be predominantly protonated. In

the IR spectra, very weak evidence of the protonated carboxyl group (COOH) exists, as

reflected by the small band of the carbonyl stretch (υC=O) at approximately 1735 cm-1

.

The next band arising from the peptide bond is the Amide II band; it consists of a 60%

C-N stretching mode coupled with N-H in-plane bending. However, in relation to the

Amide I band, this absorption does not exhibit the same wavenumber position between

the male and female fibres as highlighted by the two vertical lines. The Amide II

absorption of spectra from male fibres appears as a sharp narrow band with a peak

maximum at approximately 1511 cm-1

, while the spectral line shape from the female

fibres are somewhat broader (spectra CF1, AF17 and AF18) demonstrating an overall

shift to a higher wavenumber with a peak maximum at approximately 1515-1520 cm-1

.

The next series of absorptions in the keratin spectrum are attributed to the deformation

and bending modes of the δ(C-H), (CH2) and (CH3) groups originating from the various

amino acid (R) side chains.23 24

The bands are exemplified as medium, broad

absorptions at approximately 1461 cm-1

(shoulder peak), 1445 cm-1

and 1392 cm-1

respectively, and are quite similar in the spectra from fibres of both gender and race.

This is attributed to the lack of chemical reactivity of these groups either during natural

weathering or from cosmetic treatment.

The third commonly noted absorption arising from the peptide bond is the Amide III

band, which involves 30% C-N stretching and 30% N-H bending modes of vibrations

with additional contributions from the C-C stretch and CO in-plane bending. It exists as

a very broad band of medium intensity at approximately 1234 cm-1

.152

As per the

Amide I band, this band shows no change in the wavenumber across both gender and

race for the untreated fibres as delineated by the vertical line.

107

IR absorptions associated with the oxidation of the amino acid cystine, occur at

approximately 1200-1000 cm-1

. The bands in this region provide evidence of chemical

changes arising from oxidative damage to the fibre as a consequence of bleaching,

permanent dyeing and permanent waving. Under these conditions the cystine

disulphide cross-links are oxidised to cysteic acid (SO3-) and the oxidative

intermediates, cystine monoxide (S=O), cystine dioxide (SO2) and cysteine-S-

thiosulphate.

However, in an untreated fibre, one also expects to observe some contribution from

natural weathering. It would be virtually impossible to find a fibre that had not

undergone some form of such exposure during its lifetime. Additionally, common

physical processes such as combing and regular heating can also damage fibres as

revealed by numerous SEM and AFM studies.48 65 67

72 272

For untreated fibres discussed here, each spectrum demonstrated a weak broad shoulder

between approximately 1130-1000 cm-1

. This is attributed to the very weak S=O2

(dioxide) band at 1114 cm-1

, the S-S=O band (monoxide) at 1071 cm-1

and the –SO3-

(cysteic acid) band at 1040 cm-1

. Not generally prominent in this region for an

untreated fibre is the weak stretching band of the anti-symmetric cysteic acid at

approximately 1171 cm-1

and, the stretching vibration band of cysteine-S-thiosulphate at


.

It is observed that the cysteic acid peak is quite distinct in the spectra of the Caucasian

and Asian females, yet is rather weak and broad for the remaining spectra of the male

and female samples. This observation can be explained by the overlap of cystine

monoxide and cysteic acid bands because there is a higher concentration of cystine

monoxide than cysteic acid.

Many FT-IR studies have sampled spectra from root to tip of naturally weathered,

untreated hair fibres.52 279

They report that in the root end of the untreated fibre, the

concentration of the cystine monoxide predominates over cysteic acid whereas in the tip

the ratio is about one-to-one.279

108

Signori et al. sampled FT-IR spectra at five selected lengths from the tip of the hair

fibre, and clearly illustrated that the intensity of absorption of cysteic acid from the

middle to the tip significantly increases whereas the cysteine-S-thiosulphate band

increases only slightly.52

This phenomenon of increased acid intensity from root to tip

highlights further oxidation of cystine monoxide to cysteic acid. However, the

concentration of the intermediate, cystine dioxide, remained constant throughout the

length of the fibre.279

In fact, Hilterhaus et al. determined that the concentration of the

oxidised groups (meq/kg) in the tip end (27.6 meq/kg) is approximately double of that

in the root end (15.1 meq/kg) of the hair fibre.279

As the tip end is more exposed to the surrounding environment, it is thus more

susceptible to degradation from UV radiation, moisture and mechanical processes,

which fundamentally lead to the well-known-term as “split ends”. The same oxidative

behaviour has been detected in a multitude of different wool fibres.184

280

281

In contrast to the tip end of the fibre, the root end near the follicle is more protected and

is less subject to physical processes such as combing, towel drying, shampooing and

conditioning.

The above discussion of IR band assignments of the untreated hair provides a general

reference point with which spectra from chemically treated hairs may be compared.

3.2.1.3 Spectral Analysis of Cosmetically Treated Hair Fibres

A selection of 12 spectra from 10 typical chemically treated hair fibres and 2 atypical

chemically treated fibres presented in Figure 3.9 were obtained from both male (M) and

female (F) donors across the Caucasian (C), and Asian (A) and African-type (N) races.

The treated hair samples were selected to illustrate the effects of different cosmetic

methods that are likely to damage the structure of a keratin fibre, which could be

reflected in the IR spectra.

109

Figure 3.9 – A selection of 10 typical and 2 atypical chemically treated FTIR-ATR

spectra of human hair fibres from male (M) and female (F) donors of the major races:

Caucasian (C), Asian (A) and African-type (N).

780980118013801580

Wavenumber (cm-1

)

Ab

sorb

an

ce (

a.u

.)

CF9

NM7

NF5

NM6

NF4

AM15

AM5

AF16

AF22

CM21

CM5

CF10

1631 cm-1

1531 & 1511cm-1

1234 cm-1

C=O

1735 cm-1

1171 cm-1

1071 cm

1037 cm -1

-1

1445 cm-1

1392 cm-1

S-SO

1022 cm-1

3-1

110

The most striking difference is between both the African-type female (NF5) and male

(NM7) spectra of treated hair fibres with the other samples in the group (Figure 3.9).

These two particular sampled fibres are quite uncharacteristic of a normal α-keratin

spectrum with reference to the typical untreated fibre assignments.

To the remaining chemically treated examples in Figure 3.9, in general were spectra that

represent typical α-Keratin fibres. There appears to be no atypical bands present. These

spectra will be discussed first to set a reference for comparison with the atypical ones.

Chemical cosmetic treatments with potential to cause structural damage to fibres

include semi- and permanent dyes, bleaching and highlighting or a combination of

several of these. With the exception of semi-permanent dyes, all other treatments

involve oxidative chemical reactions to achieve the desired cosmetic outcome.

For these typical spectra (Figure 3.9), the Amide I band has a strong, broad maximum at


, which suggests an approximate shift of 4 cm-1

relative to the

spectra of the untreated fibres (ca. 1627 cm-1

). This peak shift for the Amide I band,

whether great or small, typifies a change in the secondary structure of the keratin

protein after the cosmetic process has taken place. This suggests an overall

conformational change or modification in compositional balance of the two different

forms in the fibre. The analysis of the conformation and structural modifications will be

addressed with second derivative spectra (Sections 3.3.2).

Interestingly, the Amide II band in the spectra of both male and females display similar

line shape and maxima, exhibiting a strong sharp absorption at approximately

1511 cm-1

. This observation indicates a difference from the spectral comparison of the

male and female untreated samples. Hence, the peak maximum position of the treated

female spectra exhibits a shift to lower wavenumbers compared to the untreated female

samples (i.e. from 1520-1515 cm-1

), again suggesting a change in the protein

conformation.

At pH values above 4.25, for fibres that have undergone cosmetic treatment with basic

solutions, these carboxylic acid groups would be largely in their ionised forms, resulting

in the anti-symmetric and symmetric –CO2- stretching modes at 1577 cm

-1 and

111

1400 cm-1

respectively.147 282

The spectrum of the sample, NF4, exhibits a large shift to

the left to approximately 1531 cm-1

and the peak appears sharper in contrast to the other

Amide II bands. Referring to the historical record for this sample (NF4, Appendix I), it

was noted that the individual had straightened their hair. Mentioned in Section 1.2.6,

hair straightening with NaOH can cause severe damage to the fibre by removing the

cuticle cells, exposing the underlying cortex. Therefore, it is reasonable to suggest that

the spectrum could be acquired from the cortical layer.

In the 1500-1200 cm-1

range of the treated α-Keratin spectrum, there is no significant

evidence of any shift or changes in spectral line shape of the C-H deformation and

bending modes, or Amide III band compared to the untreated spectra. This observation

strongly suggests that these molecular groups of the keratin chain, i.e. the methyl, ethyl

and conformation of the Amide III band (β sheet), are relatively stable and are suitable

internal standards for comparison of FTIR-ATR spectra as supported by the

literature.184

235-236

An important spectral region is the one that includes the cystine oxidation responses.

This is a practical indicator of cosmetic treatment. Upon close examination, the

underlying difference between the untreated and treated fibre spectra is the prominent

increase in intensity of the symmetric cysteic acid band at 1037 cm-1

. A similar effect is

also observed with the weaker anti-symmetric cysteic acid band at 1172 cm-1

, which

often appears as a shoulder of the Amide III band. The intensity of both these

absorptions are well illustrated in the spectrum, Caucasian female 9 (CF9, Appendix I),

where the individual had bleached and semi-permanently dyed the hair. As the

absorbance of the bands is quite strong, it is reasonable to suggest that the bleaching

process had been extensive.

In addition to the formation of cysteic acid, there are the simultaneous responses of the

oxidative intermediates that have not been converted in the reaction to such species as

cystine dioxide (SO2) and cystine monoxide (S=O). The S=O band is clearly evident at

1072 cm-1

whereas the SO2 absorption is negligible with a slight shoulder at 1114 cm-1

.

There is no evidence for the presence of cysteine-S-thiosulphate or Bunte salt band at

1022 cm-1

, which supports the findings by Signori et al. where it was established that

the intensity of this band increases only slightly after cosmetic treatment.52

The above

112

discussion provides a basis for the analysis of the spectra with atypical behaviour and

their comparison with the typical treated spectra. The African-type female (NF5) fibre

(Figure 3.9) shows an intense, three-pronged set of absorption bands which is observed

in the cystine oxidation region between approximately 1130-960 cm-1

. Two weak

absorptions at 922 cm-1

and 854 cm-1

are also atypical of the untreated and treated α-

keratin spectrum.

In addition, the C-H deformation bands between 1460-1380 cm-1

are much more

intense. All of these changes in the C-H and the cystine oxidation regions suggest that

the relative concentrations of those molecular fragments have increased, perhaps due to

some cosmetic surface treatment on the hair fibre.

To deduce the identity of this surface treatment, initially it was sufficient to evaluate the

details that were given at the time of sampling of the individual‟s hair (Appendix I). In

particular, the NF5 hair was permanently waved and an activator applied. This

treatment involves a chemical treatment product, which is generally formulated to

protect the hair from becoming too dry or brittle after the severe waving process.

Permanent waving of hair is one of the most complex processes of all the cosmetic

treatment methods (Section 1.2.5). It involves firstly the removal or lifting of the

cuticle with NH3 solution followed by a reduction of the disulphide cross-links by

thioglycolates or bisulphites to reduce the stability of the hair. This facilitates the hair

to be manipulated into different shapes by hot curlers or curling irons, followed by

subsequent re-oxidation of the S-S cross-links by peroxides to set the hair.58

However, permanent waving of African-type hair is rather different to Caucasoid hair in

that the hair must be straightened prior to curling. Straightening is achieved with the

use of ammonium thioglycolate and the rest of the treatment follows the normal

procedure except that sodium bromate NaBrO3 is used as a neutraliser so as not to affect

the natural colour of hair.58

As a consequence, the permanent waving process leaves the hair with decreased tensile

strength, and more brittle as well as increased porosity. Hence, directly after

completion, curl or wave activators are used, which are rich moisturising creams, to

restore the manageability, glossiness and softness normally provided by the sebum.58

113

The moisturising creams consist of many chemicals such as deionised water,

hydrocarbons, fatty acids, alcohols and esters, e.g. jojoba oil, propylene glycol,

glycerine, cetearyl alcohol, panthenol and glycol stearate.219

Therefore, in the IR spectrum, one would expect to observe the stretches, and

deformation/bending modes pertaining to the main functional groups of the constituents

of the cream. These would include the carboxylic acid (COOH), alcohol (O-H) and

ester (COOC) functional groups associated with the alkane and alkene (C-H) groups.

To investigate the hypothesis that the abnormal spectrum of the African-type female

(NF5) was a result of the use of a surface treatment such as an activator, small samples

of the fibres were cleaned according to a revised version of the IAEA method.233 234

The procedure was originally used by Cargnello et al.232

for the cleaning of

contemporary and well preserved historical hair samples in preparation for elemental

analysis. The revised procedure of the IAEA method is the same except that the

sonication times at each wash (i.e. Acetone, HPLC-grade water and deionised water)

were changed to shorter intervals of 10 minutes each. This was carried out in an

attempt to remove this so called artificial layer, to leave the surface of the hair fibre

clean. This approach also minimises any damage to the fibre.

After the fibres had been cleaned and appropriately dried for two days (Section 2.3.1.),

they were analysed by FTIR-ATR spectroscopy. After an initial analysis of the spectra

from each of the samples, it was apparent that the atypical bands were no longer

present. The spectrum approximated that of a typical treated α-keratin spectrum. As

per the typical chemically treated spectra, the Amide I and II bands have broad strong

maxima at 1631 cm-1

and 1515 cm-1

respectively, which suggested evidence of

transformation to the structural chemistry of the fibre.

Interestingly, the cysteic acid peak is apparently weak and is more or less masked by the

intensity of the cystine monoxide absorption. The low intensity of the cysteic acid band

is expected because the disulphide cross links are first reduced to thiol groups, and then

re-oxidised to as far as the monoxide unit.

114

It can also be seen that a band which is normally dominated by cysteic acid, emerges as

a weak shoulder the cysteine-S-thiosulphate as a weak shoulder at approximately

1025 cm-1

.

Thus, given that the additional bands could be attributed to the presence of a cosmetic

activator rather than the hair fibre itself, the spectrum of the cleaned fibre was

subtracted from that of the contaminated fibre (Figure 3.10).

The difference spectrum shows a broad and medium intensity band between


. Broad absorptions in this range are indicative of the

stretches of the carboxylic acid (-COOH) group and the alcohol (-OH) group. These

main functional groups are consistent with the active ingredients that are present in

wave activator applications.219

Other observed absorptions are the aliphatic C-H stretches of the saturated and

unsaturated long chain fatty acids, alcohols and esters. The absorption bands at

2944 cm-1

, 2879 cm-1

and 2829 cm-1

are attributed to the υa (CH2), υs (CH3) and υs

(CH2) stretches respectively.

Between approximately 1120-820 cm-1

, the fingerprint of molecular absorptions due to

the activator occur. The two sharp bands of medium-to-strong intensity at


and 991 cm-1

are characteristic of the O-C stretching

frequency of the ester functional group.147

The strong and broad band at 1037 cm-1

corresponds to the C-O stretching vibration of the alcohol groups present in the

chemical.147

The subsequent strong and medium absorbance bands at 993 cm-1

and 922

cm-1

can be associated with the C-H out-of-plane deformation of the alkene group

RCH=CH2 and the final band at 854 cm-1

corresponds to the δ(C-H) (med.) deformation

of the R2C=CHR alkene group.147

115

Figure 3.10 – (a) FTIR-ATR spectrum of NF5 suspected to contain a hair activator on

the surface, (b) FTIR-ATR spectrum of NF5 after cleaning of the surface and (c) the

subtraction of (b) - (a) yielding the IR spectrum of the suspicious material.

550105015502050255030503550

Wavenumber (cm-1

)

Ab

sorb

an

ce (

a.u

.)

Fibre + Activator

Original

Spectral Subtraction

COOH

C-O

CH3

3430-3090cm

O-H

1037 cm

2879 cm

2829 cm

CH2

-1

-1

-1

-1

922 cm-1

RCH=CH2

RCH=CR2

854 cm-1

(a)

(b)

(c)

116

The next assessment involves the investigation of the other atypical treated α-Keratin

spectrum which is of the African-type male fibre (NM7, Appendix I). Following the

same systematic approach as used in the previous example, analysing the individuals

“hair history”, it was noted that this fibre (NM7) had been permanently dyed and the

subject used a hair gel.

Morphologically, SEM analyses of the surface topography (section 3.1.1, Figure 3.5)

showed that moderate to severe damage was caused to the cuticle layer as a result of the

dyeing process, which utilises alkali solutions such as ammonia, to lift the cuticle and

allow the dye to penetrate the cortex.

As the individual was of African descent, the hair fibres were naturally black, but they

appeared to be dyed medium brown. With permanent dyeing, the dye remains until it is

eventually washed out, which is a period of approximately 4-6 weeks and then

colouring of proximal re-growth is required. However, in this case, there was no visible

evidence of re-growth. Thus, the dye should still have been in the cortex, and the

peripheral region of the cuticle.

For permanent dyeing to achieve brown hair from black hair, the oxidation reaction of

primary intermediates such as para-aminophenols with hydrogen peroxide form

benzoquinone monoamine. The monoimine product then reacts with couplers such as

para-aminophenols to yield the brown tri-nuclear dye, commonly referred to as

Bandrowski‟s base.283

Hence, as the dye pigment is associated with the cortex and

perhaps the lower cuticle layer, it is reasonable to suggest that the IR radiation may not

only be absorbed by the keratin protein, but also from the brown dye.

Considering the 1750-800 cm-1

region of the keratin spectrum only, the main functional

groups of the dye are the stretches of the conjugated cyclic Imine R2C=N at 1660-1480

cm-1

(very weak); the amine δ(NH2) bend at 1650-1560 cm-1

(medium) and the hydroxyl

δ(O-H) bending vibration at 1410-1260 cm-1

(medium).147

However, the Imine stretching vibrations are difficult to identify because the IR

intensity is very weak and are close to the C=C stretching vibration.147

Therefore,

probably this band will have very little impact on the spectrum especially as it is

117

situated between the strong Amide I and Amide II bands, and similarly the O-H bending

vibration which is located near the Amide III band.

Furthermore, FTIR-ATR spectroscopy is a near-surface technique only, and thus the

sampling penetration depth may not be sufficient to sample deep past the cuticle layer.

Each cuticle cell is approximately 0.5 µm – 1.0 µm thick and the overall cuticle layer

thickness varies between 5-10 layers.11 32

Hence, the average thickness of the cuticle

layer for individuals can fluctuate between 2.5 µm – 10 µm with the median being

approximately 6.25 µm or ~ 6.0 µm. The FTIR-ATR depth of penetration, dp, for a

human hair fibre between 1700-1200 cm-1

(i.e. covering the Amide I, II and III bands) is

approximately 1.24 µm – 1.75 µm (based on Equation 1.3, Section 1.6.4.).

It must also be taken into consideration that the ATR pressure tower compresses the

fibre upon sampling to facilitate good contact between the sample and the diamond IRE.

SEM studies22 23 25 26

have demonstrated that as a consequence of this sampling, the

diameter of the fibres is approximately doubled, simultaneously reducing the overall

thickness of the cuticle layer by approximately half.

Hypothetically, for a hair fibre with a cuticle thickness of 2.5 µm which is reduced to

approximately 1.25 µm upon sampling, the penetration depth of the IR radiation would

be sufficient to acquire structural information from the cortical layer. Conversely, for a

hair fibre with an average cuticle thickness of 6.0 µm, the penetration depth is

inadequate to sample structural information from the cortex. Corroborative evidence

that FTIR-ATR spectroscopy samples from the cuticle layers only is discussed in

Section 3.3, concerned with second derivative IR spectra.

In conclusion, it is reasonable to suggest that the dye pigment will have minimal impact

on any FTIR-ATR hair fibre spectra acquired from this individual‟s hair samples.

In conjunction with the suspected IR absorption of the dye, is the hair gel. As hair gel is

a cosmetic treatment that is applied externally to the hair, it is reasonable to suggest that

the gel is responsible for the abnormal spectral bands, as seen in the previous example

of the African female fibre (NM5) and the permanent wave activator.

118

An analysis of the NM7 spectrum suggests that inference appears to be valid.

Considering the spectral line shape and intensity, especially in the cystine oxidation


; these bands are very obscure and markedly different

from that of a typical treated hair fibre. In the previous assessment of the African-type

female fibre (NF5), that particular region exhibited a fork-like appearance; in this

example the equivalent region has a very broad band of medium intensity.

Additional irregularities or discrepancies from a typical treated fibre are further

illustrated by the a) intensity of the Amide III band, exhibiting a sharp maximum at

1257 cm-1

; b) a prominent, intense band at approximately 800 cm-1

and c) the

uncharacteristic broadness and line shape of the Amide II band which exhibits a shift to

higher frequency of approximately 20 cm-1

for a typical treated male fibre, associated

with an irregular shoulder at 1573 cm-1

.

Therefore, to test the hypothesis that the atypical spectrum is a consequence of the

application of an external hair gel, the questioned fibre was cleaned via the revised

IAEA method.232

This was carried out in order to remove the supposed artificial layer.

The cleaned fibre was then subsequently analysed by FTIR-ATR spectroscopy.

Immediately, it was apparent that the atypical bands had been removed from the

spectrum by the cleaning procedure. Hence, the atypical fibre was investigated further

by subtracting the cleaned fibre sample from the atypical fibre to reveal the

characteristics of the external artefact.

The result of the spectral subtraction is presented in Figure 3.11. It can be seen that the

additional vibrational bands in the atypical treated male African-type fibre are at 1260

cm-1

, 1095 cm-1

, 1020cm-1

and 800 cm-1

. A search of these bands in the literature and

by referencing to a spectral library using the OMNIC E.S.P 5.2a Spectral Software

Program, revealed that these absorptions can be attributed to the Si-CH3 (1260 cm-1

and

800 cm-1

) and Si-O (1095 cm-1

and 1020 cm-1

) stretches.147

284

These bands are part of,

and consistent with a long-chain siloxane resin, commonly seen in hair gel formulations

and fixatives such as hair sprays, activators and mousses.11

285

119

Figure 3.11 – Resultant FTIR-ATR spectral subtraction of the chemically treated NM7

spectrum minus the cleaned version of the fibre revealing the characteristic bands of a

long-chain silo-oxane resin used in hair gel and hairspray formulations.

7508509501050115012501350

Wavenumber(cm-1

)

Ab

sorb

an

ce (

a.u

.)

Si-CH 3

Si-O

Si-O

Si-CH 3

120

The findings here are consistent with a previous study performed by Bartick et al.159

The authors employed the use of Micro-ATR Spectroscopy to enhance the surface

contributions from a hair spray. By subtracting the spectrum of a clean fibre from the

spectrum from a hair spray coated fibre, the identity of the hair spray was revealed.159

In summary, this section has discussed in detail the characteristics of treated hair as

measured by FTIR-ATR spectroscopy. It was noted that in general, treated hair have

consistent spectral profiles which may be seriously perturbed by application of

specialised cosmetic surface treatments. These may be studied by spectral subtraction

which at times allows specific identification of the treatment. This information could

potentially be utilised forensically to link to a suspect‟s personal

belongings/surroundings. Therefore, the following section focuses on the application of

subtracted spectra, to discern the differences between genders for each race.

3.2.2 Analysis of Difference FTIR-ATR Spectra of Human Hair Fibres between

Gender

3.2.2.1 Spectral Differences between Genders of each Race

A number of difference spectra were obtained by subtracting typical untreated male

spectra from typical untreated female spectra for each of the three races. To minimise

the error due to differences in intensity, each spectrum had been baseline corrected and

normalised to the δ(CH2) bend at approximately 1452 cm-1

. Typical untreated fibres

were selected to understand the raw structural differences between male and female

human hair fibres. Representations of the gender differences between Caucasian, Asian

and African-type races are presented in Figures 3.12, 3.13 and 3.14 respectively. The

individual spectrum of each person was summed and averaged using the software to

obtain an average spectral profile or representation.

Beginning with the typical gender differences between Caucasian fibres (Figure 3.12),

the subtraction is of the average spectral profile of Caucasian male No. 3 from the

average spectral profile of Caucasian female No. 1 (Appendix I). The peak maxima

pertain to absorbance bands of the female fibres whereas the peak minima correspond to

absorbance bands of the male fibres.

121

Figure 3.12 – A subtraction FTIR-ATR spectrum of the average of untreated

Caucasian female No. 1 (peak maxima) minus the average of untreated Caucasian male

No. 3(peak minima).

8001000120014001600

Ab

sorb

an

ce (

a.u

.)

Wavenumber (cm-1)

CFUN1-CMUN3 CFUN1-CMUN3

1635cm

1573cm

1538cm

1469cm1396cm

1022cm1056cm

1141cm

1222cm

1330cm

1488cm

1716cm

-1

-1

-1

-1

-1

-1

-1

-1

-1

-1 -1

-1

122

Strong intensities for Caucasian female fibres were generally observed for the Amide I

and Amide II bands at approximately 1635 cm-1

and 1538 cm-1

respectively, which also

included the anti-symmetric –CO2- stretch at approximately 1573 cm

-1. The Caucasian

female fibres also showed medium intensities at 1469 cm-1

and 1396 cm-1

which are

attributed to the bending modes of the δ(C-H) and (CH3) groups respectively.

In contrast, the Caucasian male fibres generally exhibited a strong intensity of the

carbonyl, υ(C=O) stretch, of the carboxyl group (aspartic and glutamic acid) at 1716

cm-1

and medium intensities at 1488 cm-1

(δ(C-H)), 1330 cm-1

(δ(CH2) tryptophan),

1222 cm-1

(Amide III band, β-sheet) and the cystine oxidation region between


.

For the gender differences between typical untreated Asian hair fibres, the average

spectral profile of Asian male 19 (AM19) was subtracted from Asian female 17 (AF17)

(Appendix I) and is presented in Figure 3.13. The peak maxima for the females include

the anti-symmetric –CO2- stretch at approximately 1577 cm

-1, δ(C-H) 1481 cm

-1, δ(CH3)

1396 cm-1

, SO2 1133 cm-1

and SO3- 1040 cm

-1.

The final spectrum (Figure 3.14) involved the subtraction of the average spectra of

African-type male No.1 from the average spectra of African-type female No.21 as listed

in Appendix I. In this scenario, the African-type female is characterised by the random

coil and the β-pleated sheet of the Amide I band at approximately 1670 cm-1

, the anti-

symmetric –CO2- stretch at approximately 1577 cm

-1, the deformations of the C-H

bands between 1465-1376 cm-1

and the cystine oxidation region between 1122-1040

cm-1

. The vibrational bands related to the African male include the carbonyl, υ(C=O)

stretch, of the carboxyl group at 1774 cm-1

, β-pleated sheet of the Amide I band at 1616

cm-1

, tryptophan at 1550 cm-1

, the Amide II, III and IV at 1519 cm-1

, 1241 cm-1

and 979

cm-1

respectively.

123

Figure 3.13 - A subtraction FTIR-ATR spectrum of the average of untreated Asian

female No. 17 (peak maxima) minus the average of untreated Asian male No. 20 (peak

minima).

8001000120014001600

Ab

sorb

an

ce (

a.u

.)

Wavenumber (cm-1)

AFUN17-AMUN20AFUN17-AMUN20

1627 cm

1712 cm

1546 cm

1234 cm

1577 cm

1481 cm

1040 cm

1133 cm1396 cm

1419 cm

-1

-1

-1

-1

-1

-1

-1

-1

-1

-1

124

Figure 3.14 - A subtraction FTIR-ATR spectrum of the average of untreated African-

type female No. 21 (peak maxima) minus the average of untreated African-type male

No. 1 (peak minima).

125

In summary, of the spectral evidence of cosmetically treated fibres (Figure 3.9),

structural changes to the hair protein are not only specific to the disulphide linkages (as

highlighted by the increase in concentration of the cysteic acid), but are also found with

the stable peptide bonds, which are the backbone of each protein fibre. Spectral shifts

of approximately 5-10 cm-1

were observed for both the Amide I and Amide II bands

after some form of oxidative chemical treatment.

It was established that these two vibrational bands have unequal contributions of the

different conformational forms, i.e. random coil, α-helix and β-pleated sheets. The

observations suggest that the secondary structure of the fibre is transformed, such that

as one conformational form decreases another increases as a result of the chemical

treatment.

However, experimentally one is only able to illustrate these conformational changes

resulting from chemical treatment through the unravelling of the overlapped bands,

permitting those absorptions to be examined prior-to and subsequent-to the treatment

process. This work with the difference spectra leads onto the next topic concerned with

the use of second derivative spectra and its underlying importance towards its potential

as a forensic procedure for hair fibre analysis.

3.3 The Application of Derivative Spectroscopy for Interpretation of

FTIR-ATR Spectra of Single Hair Fibres

3.3.1. Optimisation of the Savitzky-Golay Method for Second Derivative Analysis

In the previous section, the main focus had been concerned with the discussion of α-

Keratin spectra in its raw form (including the use of the difference spectra). As

mentioned previously, the spectrum of α-Keratin between 1750-800 cm-1

is

exceptionally intricate, because there are many overlapping bands. This section

describes the application of second derivative spectra for the interpretation of keratin

spectra.

126

Derivative spectroscopy, particularly, where the second derivative is involved,

facilitates the unravelling of the complex overlapping bands.226

This method has to be

optimised in order to acquire the maximum information from each spectrum at high

resolution while simultaneously minimising the inherent background noise. Thus, it is

common to apply the Savitzky-Golay method (GRAMS/32AT, 6.00, Galactic Industries

Corporation, Salem, NH, U.S.A.). This approach is based on the application of an n-

degree polynomial (n = 1, 2 …) with a peak smoothing function i.e. the description of

the spectrum by a polynomial is arranged to give a compromise between smoothness of

the resulting curve and the accuracy of the fit.226

The spectral profile is approximated by a polynomial of degree, n:

y = k1 + k2x + k3x2 + … + kn+1x

n Equation 3.1

where: x = wavelength

y = signal amplitude (e.g. absorbance)

For smoothing of spectral derivatives, the order of the derivative is limited by the

degree of the polynomial used to describe the spectrum.226

Hence, for a second

derivative spectrum, a second degree polynomial was selected.

For spectral smoothing, the number of points which may be used for the smoothing

operation is a function of the experimental curve under examination. Minimum profile

distortion will occur when the polynomial accurately describes the spectrum, and will

increase as the polynomial departs from the true curve.230

The underlying rules for

selecting smoothing points are:286

(i) the number of convolution points must be an odd

number, and even points are rounded up, (ii) this number must be at least five or one

more than the degree of the polynomial (whichever is greater) and (iii) the number must

be no more than three less than the number of points in the trace. Thus, a large number

of convolution points will ultimately provide more smoothing in the result and reduce

noise.

127

Second derivative spectra of a second degree polynomial using 5, 7, 9, and 11 point

smoothing are presented in Figure 3.15. The significant absorptions in the spectra are

now delineated as minima. It is well illustrated here that as the number of smoothing

points increases, the resolution between component peaks of some of the individual

absorption bands decreases, (particularly between the Amide I and Amide II bands)

with concurrent reduction in intensity of the bands. The signal given by the 5-point

smoothing function is more intense than the signal recorded by the 11-point smoothing

function.

128

Figure 3.15 – Second derivative FTIR-ATR spectra of an untreated Caucasian female

fibre using a two degree polynomial and comparing different number of smoothing

points (5, 7, 9 and 11). Increase in smoothing points shows that resolution between the

bands decreases. Thus a 2o polynomial with 5-points was selected.

8001000120014001600

Wavenumber (cm-1

)

Ab

sorb

an

ce (

a.u

.)

5 Point Smooth 7 Point Smooth 9 Point Smooth 11 Point Smooth

5 Point

Smooth

11 Point Smooth

129

The intention of the second derivative analysis in this work is to study more deeply the

underlying differences in the α-Keratin spectrum between gender, race and the changes

that occur through the use of chemical treatment. Hence, the five points smoothing

model provides good resolution between component peaks and was selected as optimum

condition for the analysis of hair fibre spectra.

3.3.2. Assessment of Typical Second Derivative FTIR-ATR Spectra of Untreated α-

Keratin Fibres

The same untreated female and male samples that were used for the raw spectral

analysis (Figure 3.8) were selected for the untreated second derivative spectral analysis.

A typical example of an untreated second derivative spectrum (CF1, Appendix I) is

presented in Figure 3.16, and from now forth will be a reference (CFUN1) throughout

the remainder of the dissertation. The spectral differences between untreated female

and male second derivative spectra are illustrated in Figure 3.17. In general, it became

apparent that the broad peaks that were present in the raw α-Keratin spectrum were

resolved into a number of intense but sharp absorptions. In the raw spectrum

approximately 10 bands can be clearly distinguished whilst in the second derivative

spectrum approximately 20 bands can be identified.

130

Figure 3.16 – Typical untreated second derivative FTIR-ATR spectrum of hair from a

Caucasian female untreated No. 1(CFUN1).

131

Figure 3.17 – A comparison of six typical (alleged according to hair history) untreated

second, derivative FTIR-ATR spectra of hair from both male (M) and female (F) of the

Caucasian (C), Asian (A) and African-type (N) races.

8001000120014001600Wavenumber (cm

-1)

Ab

sorb

an

ce (

a.u

.)

ββ α

α

β/r

β SO3

-CH2

C-H

S=O CF1

CM3

AF17

AM19

NF20

NM1

C=O O=C-

N

COO-

CH3

SO3

- SO2αα

132

The Amide I band originally at 1627 cm-1

in the raw spectrum of Keratin, is separated

into three more distinguishable bands of unequal intensity. Thus, what appeared to be a

single absorption band is actually a number of bands of different secondary structural

forms of the protein. With reference to the spectral literature, the strong, broad

absorption at 1627 cm-1

is attributed to the carbonyl stretch, υ (C=O), of the β-pleated

sheet conformation in both the male and female spectra.

As the FTIR-ATR technique facilitates sampling of the near-surface chemistry only, the

dominance of the β-pleated sheet suggests the cuticle is comprised of rather an

amorphous matrix as opposed to a fibrous α-helical matrix that makes up the cortical

cells. This inference is supported by Church et al.184

where it has been reported that the

cuticle layer is rich in β-sheet and/or random coil forms, having a higher proportion of

cystine, proline, serine, and valine residues that have generally been considered by

Bradbury et al.287

288

as non-helical forming amino acid residues.

The second Amide I absorption, which emerges as a shoulder to the left of the β-pleated

sheet, is correlated to the υ(C=O) stretch of the α-helix confirmation at approximately

1650 cm-1

and 1647 cm-1

for the female and male spectra respectively. Interestingly,

the α-helical band for the AF17 spectrum exhibits much stronger intensity than the β-

pleated sheet, which suggests that the spectrum has been sampled from the underlying

cortex layer i.e. it has been sampled from that area. Although the historical record for

AF17 suggests that the fibre had not undergone any chemical treatment, the age of the

individual (53 years) must also be taken into account. This inference is supported by

the strong intensity of the cysteic acid band at 1041 cm-1

for this sample, which suggests

that age leads to deterioration of cystine to cysteic acid. The cuticle is removed,

exposing the cortical layer, which is ultimately reflected in the IR spectra given the

strong contributions of the α-helical Amide I and Amide II bands and carboxylic acid,

υ(C=O) stretch.

However, for most of the second derivative spectra between both gender and race, the

α-helix emerges as a shoulder or is completely absent. Explanations for the absence of

the α-helix absorption in the Amide I band exist, and are based on two separate

phenomena or a combination of these. Firstly, Kuzuhara et al.235

performed a Raman

133

spectroscopic investigation on human hair fibres and established that at a depth of about

1 µm from the fibre surface, the skeletal C-C stretch of the α-helix (normally at ca.

932 cm-1

) did not appear, which led to the suggestion that the α-helix form did not exist

in the hair cuticle.

Another plausible rationalisation for the lack of α-helical evidence is attributed to the

H-O-H bend of OHwater …..OHwater Hydrogen bond interactions, relating to adsorbed

water at approximately 1633 cm-1

. This absorption band is situated directly between the

bands attributed to the α-helix and β-pleated sheet and evidence of this band can be

observed in the AF17 spectrum. The presence of water depends on the relative

humidity (% RH) or level of cosmetic chemical treatment. As the AF17 spectrum

shows a high intensity of cysteic acid, it is reasonable to suggest that the surface is

hydrophilic, thus increasing the hydrogen bonding interaction with water molecules.

The final section of the Amide I absorption between 1750-1660 cm-1

is complex, as it is

made up of a composite of different conformational forms and amino acid contributions,

varying significantly across the gender and race related spectra.

Hair keratin is made up of a composition of the 20 different amino acids; two of those

are classified as carboxylic acid or acidic amino acids; aspartic and glutamic acid.

These acidic side chain residues thus give rise to different IR absorptions dependent

upon the pH of their environment.184

At low pH values the carboxylic acid groups

would be predominantly protonated. In the IR spectra, evidence of the protonated

carboxyl group (COOH) exists, demonstrating a sharp, yet very weak (with the

exception of AF17) band of the carbonyl stretch υ(C=O) at approximately 1736 cm-1

.184

The anti-symmetric stretch at 1577 cm-1

is scarcely below the baseline, associated with

the symmetric stretch at 1400 cm-1

which is negligible, present as a shoulder only.

These observations further strengthen the argument that the carboxyl groups are

protonated in an untreated fibre.

The penultimate absorption within this particular region of the Amide I band is assigned

to the amide (CONH2) stretch (sharp-weak) of the asparagine and glutamine side chains

at approximately 1685 cm-1

. The final of absorption of the Amide I absorption is

134

correlated with a combination of the υ(C=O) stretch (sharp, weak) of the β-pleated sheet

(1670 cm-1

) and random coil (1665 cm-1

) conformation, yielding a overall maximum at


. This is observed for both female and male spectra. However,

in some of the second derivative spectra of both male and female fibres, the two peaks

are not well resolved, and a broad, weak to medium absorption is observed at

1685 cm-1

. Apart from the presence of “free” or mobile water associated with the

surface of the fibre, absorbed or „bound‟ water is principally bonded to the hydrophilic

side chains and peptide groups and aids structural stability.

Evidence of these strong OHwater…OHwater interactions are also reflected in the IR

spectra, resulting in an O-H bending absorption band at 1693 cm-1

, justifying the

broadening and intensity in the CONH2 and β/r stretching region.268

The absorbed

water band is prominent in the AF17 sample. The presence of “bound/free” water and

relative humidity effects concerning hair keratin will be considered in the following

section.

The Amide II band has two strong, sharp peaks of different intensities at an average of

1543 cm-1

and 1511 cm-1

for the females; and 1540 cm-1

and 1511 cm-1

for the male

sources. However, with reference to the spectral literature289

, the band at 1543 cm-1

essentially consists of two bands which are ascribed to the υ(C-N) stretch (60%) and

δ(N-H) (40%) in-plane-bending of the α-helical conformation at 1545 cm-1

and the

random coil/amorphous form at 1536 cm-1

, both of medium intensity. However, with

the exception of the AM19 spectrum, the spectral evidence illustrates that there is

generally no spectral resolution between the two bands.

The strong, sharp absorption at 1511 cm-1

is directly correlated to the υ(C-N) stretch

(60%) and δ(N-H) (40%) in-plane-bending of the β-pleated sheet conformation. Once

more, the assignments suggest that the β-pleated sheet dominates the structural

conformation of the cuticle layer.184

Therefore in general, the layer is less ordered i.e.

amorphous, as opposed to the underlying cortex. However, in the CF1 and AF17

spectra, the intensity of the α-helical band is very strong. This suggests two possible

scenarios, some woman tend to have more of the α-helix in the cuticle, or the IR spectra

was sampled from the cortical layer.

135

Hence, it can be seen that the second derivative spectra of the untreated fibres has

substantiated the differences in wavelength of the Amide II band between genders

(Section 3.2.1.2), and explains the overall shift from 1511 cm-1

to 1515-1520 cm-1

for

some of the untreated female raw spectra. This occurs because the strong intensity of

the α-helical band shifts the overall peak position of the Amide II band.

The next set of absorptions between approximately 1470-1310 cm-1

is attributed to the

different deformation modes of the of the aliphatic and aromatic C-H groups which are

present in the protein structure. The sharp, weak peak at 1470 cm-1

corresponds to the

δ(C-H) deformation stretch. The subsequent peak of medium intensity at 1454 cm-1

also contains a slight shoulder at somewhat higher frequency; this is because it consists

of both bending modes of the δ(CH2) and δ(CH3) groups respectively. The bands at

1389 cm-1

and 1369 cm-1

(shoulder) are furthermore attributed to the symmetric

deformations of the δ(CH3) group.

The final two stretches within this region at 1342 cm-1

and 1315 cm-1

are most

interesting because in the normal raw spectrum they are no more than two extremely

weak peaks between the δ(C-H) deformations and the Amide III band. The band at

1342 cm-1

can be assigned to the δ(CH2) deformation bend from the amino acid

tryptophan and the band at 1315 cm-1

is the υs symmetric cystine dioxide (SO2) stretch.

The next group of spectral bands between 1300-1200 cm-1

is exclusively associated

with the Amide III mode of vibration. Weak shoulders are observed at 1284 cm-1

and

1257 cm-1

which are related to the υ (C-N) stretch (30%) and δ (N-H) (30%) in-plane-

bend of the α-helical form, respectively. However, the main band at 1235 cm-1

is

associated with the υ(C-N) stretch (30%) and δ (N-H) (30%) in-plane-bend of the β-

pleated sheet conformation, with a small contribution from the deformation of the

O=C-N bending mode.

The final part of the spectrum between 1200-1000 cm-1

contains the absorptions arising

from the oxidation of cystine with peaks at 1195 cm-1

and 1015 cm-1

corresponding to

the anti-symmetric and symmetric absorptions of cysteine-S-sulphonate; the anti-

symmetric and symmetric vibrations of cysteic acid at 1172 cm-1

and 1040 cm-1

; the

symmetric stretch of cystine dioxide at 1115 cm-1

and the symmetric stretch of cystine

136

monoxide at 1074 cm-1

. Amongst that group of absorption bands, there are a number of

very weak shoulder peaks at approximately 1151 cm-1

, 1129 cm-1

and 1084 cm-1

all of

which correspond to the stretching mode of the C-N bond. These are more active or

discernible in the Raman spectrum of α-Keratin.149

There is a lone band at approximately 933 cm-1

and it is attributed to Amide IV modes

of vibration, which primarily consists of O=C-N bending.152

In conclusion thus far, it can be seen that the chemical make-up of the α-Keratin protein

is complex, and not as simple as it appears in the raw untreated spectrum. Second

derivative spectroscopy revealed over 20 bands providing more discriminatory power to

identify the differences and similarities between single hair fibres between gender and

race.

3.2.3. Assessment of Typical Second Derivative FTIR-ATR Chemically Treated α-

Keratin Spectra

Unfortunately, as a side-effect to chemical treatment, strongly oxidising alkaline

solutions not only act on the melanin itself, but also attack the accessible reaction sites

of the protein. These include the peptide bond, hydrogen bonds, side chain amino

groups the cystine disulphide bridges. In Section 3.2.1.3, spectral evidence illustrated

that the Amide I and II bands of chemically treated fibres had exhibited shifts of


when directly compared to the spectral assignments of

untreated hairs. Thus, the transformations of the protein conformation are reflected by

the shifts observed in the FTIR-ATR spectra.

Many previous FT-IR, Raman and X-ray spectroscopy investigations have explored the

structural change in the conformation of hard keratin fibres resulting from physical

modifications (i.e. stretching and %RH) and chemical treatments. Each of the studies

utilised different conventional quantitative-qualitative approaches to deduce or illustrate

the structural modifications to the protein such as peak or curve-fitting analysis (relative

peak area intensities), Wide-angle X-ray diffraction (WAXD) and 13

C and 15

N NMR as

the chemical shifts are conformation dependent.24 235 278

289 290

137

The phenomenon of the α-β transition in hard keratin fibres was first discovered and

observed in the early 1930s and 1960s by X-ray spectroscopy.291-293

Preliminary IR and

Raman investigations were concerned with the analysis of stretched keratins such as

horsehair and wool fibres. Bendit294

and Frushour and Koenig295

respectively,

demonstrated the α-helix to β-sheet conformational transition upon stretching,

illustrating the dramatic increase in intensity particularly for the Amide I band.

Church et al.184

performed Raman and FTIR-ATR spectroscopic analyses with the aid

of mathematical software on both cortical and cuticle cells isolated from fine Merino

wool fibres. Curve fitting analysis of the deconvolution of Raman spectra of the two

layers illustrated the significant difference in relative intensities whereby the cortical

cells exhibited much higher α-helical content, while the cuticle cells were richer in the

β-sheet and/or random coil conformations.

The results further illustrated that the increases in both relative intensity and width of

the Amide I component of the cuticle cell compared to the cortical cell is a result of an

increase in disordered content at the expense of the α-helical content.184

FTIR micro-spectroscopic analyses demonstrated that the Amide I band exhibited a

significant shift of 14 cm-1

to higher wavenumber after flattening and was very similar

to IR spectra of cuticle cell fragments.184

Further, curve fitting analyses of the Amide I band performed by Lyman et al.195

and

Kreplak et al.296

on stretched horsehair suggested that physical extension gives rise to

anti-parallel β-sheet structures and is also affected by relative humidity and temperature.

With hair fibres from aged individuals, Kuzuhara et al. reported that the disulphide (-S-

S-) content of virgin black hair from Japanese females in their fifties decreased

compared with Japanese females in their twenties.297

They were able to manifest from

the curve-fitting analyses that the β/r and the α-helical contents remained constant.

Apart from the study of physical modification to the keratin fibre, a number FTIR and

Raman investigations have been carried out for the cosmetic treatment of hair and wool

fibres respectively.

138

With hair bleaching, Panayiotou24

used curve-fit analysis to examine the changes in the

keratin fibre as a result of chemical oxidation and compared those to peak areas of

untreated hair fibres. The results showed a 27 % decrease in the Amide I α-helix (from

untreated to 5 hour chemical treatment) with a simultaneous increase in the Amide I

random-coil of almost 15 % over the same time period. The Amide I β-sheet remained

relatively stable. The α-helix of the Amide II band demonstrated a 56 % decrease in

peak area after 1 hour of bleaching, but returned to 100 % after 5 hours due to the

simultaneous increase in the random coil structure. The Amide III (β-sheet) remained

relatively stable during chemical treatment.

With permanent waving, Kuzuhara et al.235 278

, Nishikawa et al.289

and Ogawa et al.290

investigated the mechanism leading to the reduction in tensile strength. Curve fitting

analyses illustrated that the β-sheet and/or random coil content (β/R) (Amide I band)

and the Amide III (β-sheet) band intensity existing throughout the cortex region

remarkably increased, while the α-helix content slightly decreased. For the Amide II

band, a slight increase at 1537 cm-1

attributed to the random coil is observed after 1

hour, in contrast with the slight decrease of the shoulder at 1545 cm-1

owing to the α-

helix structure. The absorption region of the Amide II β-sheet was scarcely changed by

the treatment.

In this investigation, to explore and highlight the conversion of the protein

conformation, a broad number of different cosmetically treated fibres were selected

from both male and female donors across the three races. The second derivative spectra

of the treated fibres were separated into mild chemical treatment and oxidative

chemically treated fibres presented in Figure 3.18 and Figure 3.19 respectively.

Figure 3.18 is a selection of four spectra from males CM6, NM6, AM5 and AM14,

which have been chosen to illustrate the general effects of mild treatment to the hair

fibre pertaining to age (CM6), physical damage (NM6) and use of surface treatments

such as hair wax and gel (AM5 and AM14).

139

Figure 3.18 - A comparison of four typical mildly treated, second derivative FTIR-ATR

spectra of hair from both male (M) and female (F) of the Caucasian (C), Asian (A) and

African-type (N) races.

8001000120014001600Wavenumber (cm

-1)

Ab

sorb

an

ce (

a.u

.)

βα

β

β

α

C=Oβ

SO

SOS=O3

3

SO2

r

CH2

CH3

-

- O=C-N

OH

OH

COO

CH2S-SO

3

-

TRP

CM6

NM6

AM5

AM14

140

The spectrum CM6 is from a 51 year old Caucasian male. The historical record

indicates that the hair has started to grey. Kuzuhara et al. studied the internal structure

changes in virgin black hair fibres due to aging using Raman spectroscopy.297

Spectra

were acquired from eight females in their mid-twenties and compared to eight females

in their mid-fifties. The spectral evidence demonstrated that the cystine content

decreased with the increase in age as illustrated by the reduction in intensity of the

disulphide (-S-S-) stretch.297

Hordern298

and Panayiotou24

focused on the FTIR spectroscopic analysis of black and

melanin poor-to-white hair fibres from the scalps of the same individuals. The melanin

poor fibres demonstrated higher levels of Cysteic acid and black hair fibres showed

stronger Amide I and Amide II bands, thus supporting the findings by Kuzuhara et al.297

It was suggested that because melanin‟s principal role is to protect the hair fibre

proteins from ionising radiation of the sun's ultraviolet rays via preferential oxidation

(due to a high electron density); grey-to-white fibres which are melanin deficient are

therefore more susceptible to cystine oxidation, resulting in the production of increased

levels of cysteic acid.298

Evidence of aging in this fibre can be seen based on the strong intensity of the cysteic

band at 1041 cm-1

relative to the samples.

The spectrum NM6 is from an 18 year old African American male who uses a hair

moisturiser. In Section 3.1.1.2, an SEM image of this fibre showed severely jagged and

chipped cuticle edges as well as up-lifting of the cuticle cells, exposing the underlying

cortical layer. The damage was ascribed to physical processes such as grooming.

The spectrum displays little evidence of oxidative treatment based on the weak intensity

of the cysteic band at 1041 cm-1

. The intensities of the α-helix for the Amide I and

Amide II band are stronger than the β-sheet, which suggests that the spectrum has been

acquired from the cortex layer which has been exposed due to the physical damage.

This is supported by the strong intensity of the υ(C=O) stretch from the acidic amino

acids, where the concentrations are higher in the cortex than the cuticle. The spectrum

141

also indicates a strong presence of water based on the intensity of the absorbed H2O

bend at 1693 cm-1

.

The final two spectra in Figure 3.18 are from Asian males AM5 and AM14 who use

wax and hair gel respectively. In Section 3.2.1.4, the analysis of the atypical treated

fibres illustrated that the use of surface treatments could affect the absorption spectrum

of keratin. However, in these two examples there are no irregular bands present other

than those pertaining to the keratin spectrum. The presence of external treatments

depends upon the time the product was last applied, when the hair was last cleaned and

the amount that is applied to the fibre. Both spectra exhibit the presence of water based

on the broad intensity of the Amide I, β-sheet conformation at 1633 cm-1

.

Figure 3.19 is a selection of 7 spectra from individuals AF3, AF16, CF9, CF10, CM21,

CF20 and NF41 which have been chosen to highlight the effects of cosmetic chemical

treatment. The spectra are in order (bottom to top) from weak to strong oxidative

chemical treatment. The spectrum of Asian female No. 3 is of a fibre that has been

treated with a semi-permanent dye. As mentioned in Section 3.2.1.3, the semi-

permanent dye is unlikely to be observed in FTIR-ATR spectroscopy as the dye-

pigment penetrates deep into the cortex layer. The strong intensity of the cysteic acid

band and the age of the Asian woman (40 years of age) suggest that the melanin

pigment is starting to be chemically reduced thus increasing its susceptibility to UVA

and UVB radiation to impair and oxidise the –S-S- bond. The fibre samples received

from the Asian female No. 16 have been permanently dyed in conjunction with

“frosting” or bleaching of the tip towards the shaft which are both oxidative procedures.

This is noticeably discernible by the strong intensity of the cysteic acid band and the

weak intensity of free carboxylic acid (COO-) group. As the cuticle scale has been

lifted or perchance removed to allow the dye pigment to enter the cortex, the intensity

of the α-helix has increased as the cortex of this conformational form.

142

Figure 3.19 - A comparison of seven typical chemically treated, second derivative

FTIR-ATR spectra of hair from both male (M) and female (F) of the Caucasian (C),

Asian (A) and African-type (N) races.

8001000120014001600

Wavenumber (cm-1

)

Ab

sorb

an

ce (

a.u

.)

β β

ββ

αα

C=O S=OC-HCH

2CH

3

SO2

SO3-

COO

SO3

-r

-

O=C-N

OH

OH

CH2

S-SO3

-

TRP

NF41

CF20

CM21

CF10

CF9

AF3

AF16

143

The FTIR-ATR spectrum of Caucasian female No. 9 exhibits severe damage which

illustrates the most intense level of cysteic acid of the 66 persons which spectra were

acquired from. The intensity of this band can be attributed to a number of factors. The

hair fibre is white blond which has been bleached with concomitant photo-oxidative

bleaching from periodic tanning in the sun because of a decrease in melanin pigment as

the subject is 53 years of age. Thus, the hair fibre is highly hydrophilic as denoted by

the lack of resolution of the Amide I band. This evidence can be correlated to the

microscopic evidence, Figure 3.7; Section 3.1.1.2, which highlights severe lifting of the

cuticle scales associated with smoothing of the cuticle layer for the cuticle scales on the

uppermost layer should be slightly tilted.

According to the “hair histories” of CF9 and CF10 (Appendix I), their particulars are

almost identical except that the individual, CF10, spends minimal time outdoors which

illustrates the strength of photo-oxidative bleaching from harmful UVA and UVB rays in

Brisbane, Australia. Therefore, as the CF10 spectrum represents a typical example of

chemical treatment it will now forth be referred to as CFTR10 (TR = treated) for

reference purposes.

The spectrum of CM21 is very similar to that of CF10 with a slight increase in cysteic

acid and the amino acid tryptophan emerges as a shoulder peak to the left of the Amide

II α-helical band. The final two spectra, CF20 and NF4 have been purposefully chosen

to demonstrate the “top-end” and most damaging of the chemical cosmetic treatment

scale. The spectrum of CF20 is of an 18 year Hispanic woman who has had their hair

permanently-waved. The chemical process as outlined in Section 1.2.5.1, explains that

the cuticle scales are lifted to allow the reductive solution to reach the cortex with

concomitant cleavage of the disulphide linkages. Oxidising agents are then used to

reform the links and the cuticle scales return to their original position. However, the

second derivative FTIR-ATR spectrum contradicts this supposition. The intensity of α-

helix, of both the Amide I and Amide II bands, have increased dramatically more so in

the Amide II bands which suggests that layers of the cuticle have been peeled free of the

fibre allowing the evanescent wave of the IR radiation to penetrate the cortical layer,

which is rich in the α-helix conformation.

144

It is apparent that ammonium hydroxide has reacted with the acidic aspartic and

glutamic amino acid side chains, which is noticeably discernible by the strong intensity

of the free carboxylic acid (COO-) group at approximately 1573 cm

-1. The strong

intensity of the cysteic acid band reveals that the oxidising agent used has not fully

reformed the disulphide cross-links.

Finally, NF4 is a spectrum of a 24 year woman from Ghana who had her hair

straightened/relaxed and used hair spray to hold it in place. Straightening African-type

hair is different to Asian and Caucasian hair because the hair is chemically treated with

sodium hydroxide to cleave the disulphide bonds whereas Asian and Caucasian hair can

be straightened with a straightening iron. Again, there is strong evidence to suggest that

the treatment has been severe by stripping off layers of the cuticle as exemplified by the

strong intensities of the α-helix and free carboxylate group of the acidic amino acids.

The comparison of the FTIR-ATR vibrational bands assignments for this investigation

is summarised in Table 3.1. These results are compared against literature values and the

IR vibrational band assignments using FTIR Micro-spectroscopy from previous

investigations.

145

Table 3.1 – Major Vibrational Band Assignments of Human Hair Keratin

Assignments Literature

Values

(cm-1

)152

Previous

Investigation

(cm-1

)24

Current

Investigation

(cm-1

) (ATR)

Amide I

80 % C=O stretch

C-N stretch

C-CN

1690-1600

1670

1650

1669

1631-1627

Amide II

60 % C-N stretch

40 % N-H in plane bend

Minor contributions C-C,

N-C stretch, C=O in plane

bend

1575-1480

1545

1532

1548

1534

1517

1580-1481

1520-1511

(C-H) deformation bend 1471-1460 1470 1461

δ(CH2) deformation bend 1453-1443 1453 1445

δ(CH3) deformation bend 1411-1399 1397 1392

Amide III

30 % N-H in plane bend

30 % C-N stretch

Contributions from C-C

stretch, C=O in plane bend

1320-1210

1260-155

1241-1231

1225

1311

1239

1322-1211

1234

S

O

O

S

Cystine Dioxide stretch

1121 1121 1114

S

O

S

Cystine Monoxide stretch

1071 1072 1071

-SO3-Cysteic Acid stretch

1040 1041 1037

146

3.3 Chapter Conclusions

Investigating the surface topography of both untreated and chemically treated human

hairs at a microscopic level, has provided a basis for the understanding of the chemistry

of keratin fibre on a structural level. In general, the SEM analyses of the surface

topography suggest that a hair fibre can be of three types:

(1) Untreated fibres with relatively negligible damage to the cuticle,

(2) Mildly Treated fibres which are a result of physical/chemical treatment

and show moderate chipping and jaggedness of the cuticle edges and,

(3) Chemically Treated fibres as a consequence of oxidative chemical

reactions and display the highest amount of damage to the cuticle,

and also;

(4) Combing or maintenance of African-type hair is difficult and abrasive,

(5) Chemical damage along the fibre appears to be random from root to tip.

The comparison of male-female untreated and treated hair using raw and second

derivative FTIR-ATR spectra highlighted the conformational transformation of the α-

helical protein to the β-pleated sheet and random coil conformation as a consequence

of cosmetic chemical treatment/s. Untreated male spectra exhibit greater intensity of

the β-sheet with a maximum at 1511 cm-1

(Amide II) whilst females exhibit more of the

α-helical conformation with a maximum between 1520-1515 cm-1

. However, through

chemical treatment, the α-helix is untwisted to the β-sheet formation which results in a

peak shift to 1511 cm-1

. Difference spectra between male and female fibres within each

race suggest that female spectra exhibit greater intensity of the amino acids

tryphtophan (1554 cm-1

) and aspartic and glutamic acid (1577 cm-1

).

In general FTIR-ATR spectra showed the dominance of the β-pleated sheet, which

suggests the cuticle is comprised of an amorphous matrix as opposed to a fibrous α-

helical matrix that makes up the cortical cells. The morphological and structural

similarities and differences of untreated, mildly treated and treated fibres have provided

a foundation on which the statistical data can be corroborated within the subsequent

chapters.

147

4.0 FORENSIC PROTOCOL FOR ANALYSING HUMAN HAIR

FIBRES USING FTIR-ATR SPECTROSCOPY WITH THE AID OF

CHEMOMETRICS AND MCDM

Panayiotou24

endeavoured to expand her preliminary findings vis-à-vis the

discrimination of single hair fibres, which were concerned with object discrimination on

the basis of chemical treatment, gender and major race. The intention was to develop a

forensic protocol, which is a formal procedure, intended to be followed by forensic

scientists when analysing single human hair fibres. The protocol design involved a

systematic approach to analysing recovered unknown single hair fibres from crime

scenes with the use of FT-IR micro-spectroscopy and interpreting the spectral data with

the use of chemometrics. It was envisaged that in the future, the protocol would be used

in conjunction with current and legally accepted techniques such as microscopy and

DNA analysis. More importantly, it was proposed that the combination of these three

techniques would enable improved identification of a hair profile, as there would be

information on the morphological, molecular and genetic properties.

In general, when taking an unknown fibre from a crime scene, it is first necessary to

compare it microscopically to control fibres from the victim or the alleged suspect (if

available) for association purposes. If the questioned fibres are believed to be different

based on their morphological features, then, on that basis those accused person/s are

excluded from further examination and scrutiny from investigators. If however, the hair

fibres are found to be similar in morphological appearance, the fibres are then examined

further to remove the subjective nature of the microscopic analysis, for which the

conclusions provide circumstantial evidence only. DNA may be present around the

follicular sheath which is generally present where the hair fibre has been forcibly

removed. However, the majority of fibres found at crimes scenes are naturally shed (i.e.

in the telogen phase) and contain no root. Therefore minimal nuclear DNA is present,

only mitochondrial DNA (in the hair shaft) which is inherited through the maternal

lineage.

148

In conjunction with morphological and DNA analyses, it is also feasible to execute FT-

IR spectroscopic measurements on the questioned fibres to gather structural molecular

information. Such spectra are examined in conjunction with control fibres.

In the instances where no control fibres are available, or where the questioned fibres

cannot be matched when compared to control fibres, then the forensic scientist can still

employ FT-IR spectroscopy with the aid of chemometrics whilst following a strict

forensic protocol. The results of this analysis provide the investigating police officers

with a hair profile identification, supplying them with information on the race, gender

and cosmetic treatment (if any) of the alleged suspect‟s hair.

4.1 The Protocol – A Systematic Approach to Hair Fibre Analysis

In the ideal case, the forensic crime scene officer collects unknown hair fibres from the

victim and the immediate surroundings of the body.

Before any spectral analysis is carried out on the questioned hair fibre, it is imperative

that a suitable spectral database or reference set is assembled, which covers a wide

range of individuals of known background/history. Variables such as race and ethnic

background; age; cosmetic chemical treatments; including level of sun exposure,

medication and even social activities such as swimming in chlorinated or saltwater must

all be considered. This information is necessary because it builds up an individual‟s

“hair history” that provides evidence, which may aid in the identification process.

With the reference set in place, spectra can be sampled from the unknown fibre and

together with the reference spectra, can subsequently be processed by chemometrics for

comparison. The flow diagram (Figure 4.1) outlines the methodology for the

investigating forensic scientist to follow so as to determine the origin of the unknown

hair fibre. In the first instance the spectra are processed using chemometrics and

submitted to PCA for pattern recognition (i.e. comparison/discrimination), loadings

analysis (i.e. variable separation/s) to justify the basis of the separation, and Fuzzy

Clustering for spectral classification.

149

Figure 4.1 – The proposed forensic protocol

24, for the analysis of unknown

hair fibres using FTIR spectroscopy and Chemometrics with the inclusion

of the novel African-type group (green).

Asian

Unknown Hair

Fibre

Chemical

Treatment

Yes No

Gender Gender

Female Male

Race Race

Caucasian Asian Caucasian Asian Caucasian Asian Caucasian

Race Race

Female Male

African-type African-type African-type African-

type

150

PCA facilitates the user to observe clustering between certain scores (objects i.e.

spectra) and simultaneously highlights the discrimination or discrepancies between

individual groups, allowing inferences and conclusions about the relationships and

associations to be established on this basis. Further information/evidence can be

obtained from loadings (weightings) plots where the values of the scores are plotted

against variables (i.e. wavenumbers cm-1

), highlighting which variables have significant

weighting on a PC (positive or negative) and also indicates which objects are strongly

related to those variables.

The first separation of the scores is on the basis of chemical treatment. For example, if

it had been established that the unknown hair fibre had not been chemically treated, all

the untreated reference spectra including the unknown fibres are taken from the data

matrix and subsequently processed again, whilst the chemically treated spectra are

excluded from further analysis.

The computation of the new data subset then separates the spectra on the basis of

gender, and for arguments sake it has been recognised that the unknown fibres have

originated from a male individual. Thus, taking all the reference untreated male and

unknown/suspect male spectra and compiling another novel subset, subsequent

processing illustrates the final separation is on the basis of major race (i.e. Asian,

Caucasian or African-type).

Unfortunately, the original protocol design suffered from some significant limitations as

were outlined in section 1.5.3.3. However, the major deficiency present that affected

the potential of the protocol and which required major consideration was the fact that

Panayiotou24

did not incorporate African-type hair fibres into the methodology, nor had

such hairs been studied spectroscopically in previous studies. Hence, this protocol

excluded a significant portion of the population globally and is therefore restrictive.

Barton23

studied African-type hair fibres and attempted to re-construct the proposed

protocol to include this class. Thus, it appeared that human hair spectra could be

separated on the basis of race, gender and chemical treatment, which validated the

prospective protocol methodology with but one unusual exception (section 1.5.4.1).

When the African-type hair fibres were partitioned on the basis of chemical treatment,

151

the spectra of untreated African-type hair fibres behaved similarly to Asian and

Caucasian chemically treated spectra, while some chemically treated African-type hair

fibres displayed similar properties to Asian and Caucasian untreated hair fibres. This is

an obvious contradiction to the separations observed for the Caucasian and Asian

fibres.152

However, Barton‟s23

African-type fibre sample set contained hair from only eight

individuals, and could only be considered as a guideline.

Hence, it is reasonable to suggest that the current protocol methodology has only a basic

skeletal framework, which requires improvement to become a comprehensive

identification procedure. At this stage, certainty has only been given to FTIR-ATR

spectroscopy over Micro-spectroscopy as an acceptable technique for acquiring spectral

data based on improved spectral quality. Warranting further investigation however, is a

meticulous analysis of the protocol design itself, where ambiguity remains between the

separation of spectra of male and female fibres and between each of the races. This

specifically refers to the principal differences in the conformational structural

chemistry.

Hence, this chapter deals with a detailed investigation of analysing human hair

fibres by FTIR-ATR spectroscopy aided by Chemometrics and MCDM. The aim

ultimately is to design a forensic protocol which would cover the hair

characteristics from the three races – Asian, Caucasian and African-type. The

following issues from previous and present investigations were addressed,

specifically:

a) To explore the potential spectral regions that could provide optimum

discrimination of FTIR-ATR keratin spectra (i.e. the entire fingerprint


and/or different combinations of the Amide I,

II and III bands only).

b) To incorporate other chemometric techniques for classification, namely

Fuzzy Clustering, for the identification of specific classes of spectra i.e.

untreated and chemically treated hair fibres.

152

c) To apply multi-criteria decision making (MCDM) techniques to rank-

order the spectra (PROMETHEE) and examine the relationship within and

between the classes (GAIA).

d) To compare the second derivative FTIR-ATR keratin spectra and with

zero order raw spectra for the proposed protocol.

On completion of the above aims, the objective is to then use the optimum chemometric

conditions to investigate the potential of the protocol as a viable hair fibre identification

procedure.

4.2 Optimisation of the Proposed Forensic Protocol for Spectroscopic

Analysis of Human Hair Fibres with the aid of Chemometrics

A spectral database was obtained from 66 individuals of known hair history (Appendix

I). In total, the database contained 550 spectra acquired from 2-3 randomly selected

fibres (depending on the length) from each individual, where 3-5 spectra (again,

depending on the length) were recorded, in close proximity, along the shaft (i.e. root to

middle) of the fibre. The number of fibres examined is less than what would be selected

by a forensic examiner, but it must also be taken into consideration that the aim of the

investigation was to initially build a database on single or minimal hair fibres and then

expand and diversify the protocol appropriately, based on the conclusions.

These 550 spectra were further classified into untreated and chemically treated groups

according to the hair history survey. Spectra (350, 170 African-type, 90 Caucasian and

90 Asian spectra) were acquired from individuals with untreated hair (i.e. no chemical

treatments or use of external products such as gels, waxes or moisturisers). Conversely,

the chemically treated database is based on 200 spectra originating from 90 African-

type, 40 Caucasian and 70 Asian spectra, again with an approximate balance between

genders within each race.

153

The raw data matrix was double-centred and the resultant matrix was submitted to PCA

(Section 2.6).

4.2.1 Spectral Regions and Fibre Discrimination

4.2.1.1 Spectral Range 1750-800 cm-1

The analysis of FTIR-ATR spectra by chemometrics focused on the wavenumber region

within 1750-800 cm-1

.22-24 26

This included the Amide bands (I, II, and III), δ(C-H)

deformations and the cystine oxidation region. In earlier investigations 22

24 152

, this

spectral region has proven to be successful for the separation of individuals on the basis

of chemical treatment, gender and race (Caucasian and Asian hair fibres only).

However, more recent studies23

have suggested that there may be some ambiguity

between spectra from untreated and chemically treated fibres, especially those from

African-type hair.

The uncertainty arises from the fact that although individuals claim in their hair fibre

histories that their hair has not been subject to any form of cosmetic chemical treatment,

their hair may in-fact have undergone some form of physical/mechanical stress or

photo-chemical oxidation. These processes include moderate to severe bleaching by

UVA and UVB radiation from sunlight (and excessive tanning) resulting in fission of the

C-S bond; damage to the cuticle surface from rigorous combing, shampooing and towel

drying; and the excessive use of hot curling and straightening irons which contributes to

the breakage of the disulphide (S-S) linkages. These phenomena and inferences have

been observed and well supported in the literature by a number of SEM examinations

pertaining to those specific effects.6 11 48

52

60

72

Ultimately, these unmanageable occurrences result in increasing the concentration of

cysteic acid and reactive intermediates within the cuticle and cortical layers as damage

to the protective surface layers, exposing underlying layers, rendering the fibre

susceptible to chemical structure modifications.

This raises the unremitting issue of the discrimination of untreated and chemically

treated hair fibres. Hence, it is important to investigate the spectral region between

154

1200-800 cm-1

(i.e. cystine oxidation region) and its importance or otherwise for the

discrimination between human hair fibres (with the inclusion of African-type hair

fibres) for the forensic protocol.

Initially, when the entire spectral database was processed, the PCA PC1 vs. PC2 scores-

scores plot for the 1750-800 cm-1

wavenumber region appeared complex. This plot

showed that there were significant atypical spectra present from specific individuals.

These objects influenced the core group to cluster heavily around the origin. The

atypical spectra originated from the hair fibres of the African-type female number

(NF5) and African-type male number (NM7) (Appendix I). They were analysed in the

previous chapter (Section 3.2.1.3), and it was established that those individuals had

utilised external surface treatments such as hair gels, hairspray and moisturisers.

Clearly, the constituents of these treatments would contribute to their IR spectra and

hence, distinguish them from the typical untreated hair spectra. It would be noted that

the amounts of such treatment need not be large so as to be easily detected in the

spectra.

As a result, for the purposes of the protocol concerning questioned fibres, the hair fibres

must not be enclosed by an external layer of a cosmetic hair product or any debris that

may have adhered to the fibre (e.g. through burial). For example, if fibres are located at

grave/burial sites, depending on the environmental surroundings they will contain

numerous aggregates of soil particles, micro-organisms, fungal hyphae and debris.23

Fortunately, cleaning/washing methods of hair fibres have been trialled in a number of

past investigations23 25 233 234

where it has been established that the revised acetone-water

method recommended by the IAEA (Section 2.3.1) is the most efficient. These studies

have also suggested that time, intensity and type of sonication are very significant for

the cleaning methodology of human hair fibres.233 234

These investigations have

illustrated that short time periods at low intensity in a sonication bath are vital in

maintaining the integrity of the cuticle layer morphology. Hence, if a hair fibre displays

atypical structural behaviour from the keratin protein, it firstly must be cleaned before

being processed and compared to a spectral database.

155

These atypical samples were removed and the database was processed again yielding

another PC1 vs. PC2 scores-scores plot (Figure 4.2). In total, 86.5 % spectral data

variance is explained by the first two PCs with 75.7 % variance on PC1 and 10.8 %

variance on PC2.

Figure 4.2 - PCA scores plot of PC1 (75.7 %) vs. PC2 (10.8 %) of the untreated fibres

(blue), the chemically treated fibres (pink) and the entire African-type fibre database

(green) using the traditional spectral region between 1750-800 cm-1

.

This new plot showed an intense cluster of spectra (denoted by the arbitrary elliptical

circle) with low-to-moderate scores on both the positive PC1 and PC2 axes. This group

contained the majority of the African-type fibre (green scores) spectral objects of both

untreated and cosmetically treated spectra. Hence, although an original African-type

spectral subset has been added to the entire database, no distinct separation of untreated

and chemically treated African-type spectra was evident because of the intense

clustering. This trend of the African-type fibre spectra is consistent with the previous

investigation.23

Furthermore, the plot shows little evidence for the discrimination

between untreated (denoted in blue) and chemically treated (denoted in pink) fibres

when the African-type fibres were included.

-35

-30

-25

-20

-15

-10

-5

0

5

10

15

-50 -40 -30 -20 -10 0 10 20 30 40

PC

2 (

10

.8%

)

PC1 (75.7%)

Untreated Treated African-type

156

This phenomenon is inconsistent with the protocol, and thus, the African-type spectra

will be addressed independently in the subsequent chapter in order to avoid any further

confusion regarding the separations/associations between untreated and chemically

treated Caucasian and Asian spectral objects. Hence, the work described in the rest of

this chapter will be to assemble an “ideal” spectral data matrix based on typical samples

from the Caucasian and Asian subjects. Such data could be used as a reference set for

comparison with untreated-treated African-type fibres or those of unknown origin.

Thus, the remaining Asian and Caucasian spectra (292 spectra) were processed to

produce a PCA scores-scores plot (Figure 4.3). Overall, 89.2 % spectral data variance

was explained by the first two PCs with 74.8 % variance on PC1 and 14.4 % on PC2. It

appears that on the PC1 axis, there is a slight trend for the separation of untreated hair

fibre spectra (blue) with negative scores on PC1 from chemically treated hair fibre

spectra (pink) with positive scores. This is broadly consistent with previous

investigations.22-24


(blue) and the chemically treated fibres (pink) of Caucasian and Asian fibres between

1750-800 cm-1

.

-30

-25

-20

-15

-10

-5

0

5

10

15

-50 -40 -30 -20 -10 0 10 20 30 40

PC1 (74.8%)

PC

2 (

14

.4%

)

Untreated Chemically Treated

Caucasian Female Untreated

CFUN 1

Caucasian Female Treated

CFTR 10

157

This inference is supported by the locality of the typical untreated and treated female

spectra that were designated as the reference spectra for each group in the previous

chapter. The untreated Caucasian female (CFUN 1) spectral objects have moderate to

high scores on negative PC1 whilst the treated Caucasian female (CFTR 10) objects

have moderate to high scores on positive PC1. Hence, the large variance between the

two groups reflects the difference of the structural chemistry between them. However,

it is clear that there are scores from both spectral object sets which overlap each other.

Thus, 34 chemically treated spectral objects were associated with the untreated ones,

whilst 29 untreated spectral objects were overlapping the chemically treated spectral

group. It is unlikely that over sixty fibres were wrongly sampled and measured i.e. they

are unlikely to be outliers. Rather, it is more likely that they are atypical objects, which

brings into question the reliability of the collected „hair histories‟ collected from the

donors, and consequently, their use for the classification of the fibres.

Nevertheless, the explanation for the above misclassification of the fibre spectra is

twofold. To begin with, for untreated fibres to demonstrate similar characteristics to

those of chemically treated fibres, where the individual claims to not have used

cosmetic enhancement, the justification may possibly be a combination of

physical/mechanical processes and area of sampling of the fibre.

Reiterating, many FT-IR spectroscopic studies have shown that the levels of cysteic

acid and cystine monoxide increase along the length of the fibre from the root to tip.

This is a result of weathering processes such as sun bleaching or photo-oxidative attack5

51 52

279

, or physical processes such as brushing, combing, styling with hot straightening

and curling irons, shampooing and towel drying.48 65

272

Using chemometrics, Panayiotou 22

was able to discriminate between spectra collected

at the root, middle and tip off a fibre; illustrating that spectra pertaining to those

sampling areas were chemically different, based principally on the amount of cysteic

acid in the fibre. Spectra sampled from the root and the middle (shaft) of the fibre was

separated along the PC1 axis from spectra sampled at the tip. Furthermore, spectra

from the root were separated from the shaft spectra along the PC2 axis. Hence some of

the untreated fibres behaved as outliers because the spectra have been sampled from a

region between the shaft and tip of the fibre, where cysteic acid concentration is higher.

158

Of particular interest is the majority of the untreated Caucasian male fibre spectra which

displayed scores in this dense cluster located on positive PC1 and PC2. The spectra

originated from 5 individuals (Caucasian males 4-8 in Appendix I) of European origin,

with ages varying from 23 to 54. The commonality between the samples donated by the

last three mature male subjects was that they were between 50 and 55 years old and that

their hair has proceeded to grey and/or whiten, suggesting that age, weathering and

deterioration/absence of melanin pigment are responsible for the association of the

spectral objects with chemically treated spectral objects on positive PC1.

Reiterating from the previous chapter (Section 3.3.2), Kuzuhara et al. studied the

internal structure changes in virgin black hair fibres as a function of age with the use of

Raman spectroscopy.297

FTIR studies with the aid of Chemometrics24 298

focused on the

analysis of black and melanin poor-to-white hair fibres from the scalps of the same

individuals. Hordern298

established from PCA that Caucasian male, black or melanin

rich fibres could be discriminated from those of the Caucasian male white hair fibres

from the same individuals along the PC2 axis. Corroborative evidence from the PC2

Loadings plot298

described that the separation was on the basis of white fibres

demonstrating higher levels of Cysteic acid and black hair fibres exemplifying stronger

Amide I and Amide II bands, thus supporting the findings by Kuzuhara et al.

Hence, grey-to-white fibres which are melanin deficient appeared to be more

susceptible to cystine oxidation, resulting in the production of increased levels of

cysteic acid.

The hair samples donated by the relatively younger Caucasian male donors are short in

length which means that the boundaries (i.e. root, shaft and tip) along the length of the

fibre are also shorter. Spectra were likely to have been sampled towards the tip end of

the fibre. This suggests that the area of sampling along the fibre is a contributing factor

in the discrimination of untreated hair fibres and also a logical explanation for their

presence with chemically treated fibres in Figure 4.3.

Alternatively, the grouping of cosmetically treated spectral objects with untreated

spectral objects can also be rationalised. Although an individual may claim to have

159

performed a variety of cosmetic enhancements to their hair, the separation is primarily

dependent on the time since the chemical treatment had been carried out.

In the scalp, each hair grows progressively at rate of approximately 1 cm per month.32

Hence as the hair fibre grows, the natural melanin pigment is gradually restored from

the root to the tip (i.e. regrowth), coupled with the reformation of the stable cystine

disulphide links that return mechanical stability to the fibre.5 Simultaneously, for hair

dyeing, permanent and semi-permanent dyes are slowly washed out of the hair fibre

which is a process that may take up to six weeks.54

Therefore, at the time of sampling if

the individual states that the cosmetic process had taken place at least 6-8 weeks prior to

sampling, then chemically the fibre would have lower concentrations of cysteic acid,

cystine monoxide and cystine dioxide, thus the spectra would display characteristics

similar to that of an untreated fibre.

To resolve the difficulty of identifying the spectral objects, other methods of

classification were applied to investigate the possibility of the presence of other classes

of hair fibre. Fuzzy Clustering (FC, Section 2.7.3.1) method was applied initially to

explore how many classes may be present in the data matrix. SIMCA was not as

practical because the user has to nominate members of the classes.244

Hence, the Caucasian and Asian spectral database was submitted to FC for modelling.

A three-cluster model was calculated with a hard weighting (p = 1.2) based on 4 PCs

(96 % data variance). A simple three cluster model was selected allowing, at this stage,

for just one other class apart from the untreated and treated fibre. SEM images (Section

3.1) and second derivative spectra (Section 3.3) suggested that a third (intermediate)

type of hair fibre existed in nature. The p exponents were chosen so that the results

were comparable and consistent with FC results of previous investigations.22

24 152

The FC membership values for Classes 1, 2 and 3 for hard clustering (p=1.2) are

presented in Appendix II. With reference to the CFUN1 samples (typical untreated

fibres), the table illustrates that these spectral objects (blue) display membership values

of 1 or close to one with a hard exponent in Class 3.

160

Alternatively, with reference to the class membership values of CFTR10 (typical

chemically treated fibres; pink) exhibit values of 1 or close to one with a hard exponent

in Class 2.

The third cluster (Class 1, green), can be attributed to hair samples that have not been

subjected to oxidative cosmetic treatment but have been subjected to either mild

chemical treatment having moderate levels of cysteic acid due to age, section of the

fibre sampled or intense photo-chemical oxidation, surface treatment from gels and

waxes, or experienced physical treatment from rigorous grooming. Therefore, the third

cluster has provided strong evidence that a third class of hair fibre exists, with the

possibility of sub-classes (i.e. mild physical or mild chemical), and has not yet been

thoroughly investigated.

In addition to the three classes, as the FC modelling suggests some fibres demonstrated

„fuzzy‟ membership with values that vary between 0-1 across the extremes for the three

clusters (white). Two types of „fuzzy‟ membership exist in Appendix II. The first type

of „fuzzy‟ membership can be observed with fibres that pertain to Asian male, AM19

(i.e. AM191 – AM199 = three fibres with three spectra from each), which claimed to

have had no prior chemical treatment. The FC membership values suggest that one

fibre that was sampled is treated and the other fibres sampled is untreated.

The second type, which makes up the majority of the „fuzzy‟ class, illustrates that

spectra acquired from the same fibre demonstrate membership of all three classes. This

type of „fuzziness‟ most likely occurred because spectra were sampled at different

locations along the length of the fibre. In total, 116 of the 292 spectra displayed fuzzy

membership which is approximately 40 % of the Asian and Caucasian spectral database.

The fuzzy membership of some individual fibres illustrated that each hair sampled

randomly from the scalp of an individual may be different chemically, due to moderate-

to-harsh weathering from chemical or physical processes. Fibre position and time since

cosmetic treatment are contributing factors to the misclassification of the overall

chemical state (untreated vs. treated) of each hair fibre on the basis of „hair treatment

history‟ as supported by the hair donor.

161

Therefore, it is suggested that a larger number of samples should be randomly selected

from an individual‟s scalp to compensate for the variance and reduce the amount of

“fuzziness”. However, it was not within the scope and timeframe of the project to

analyse many fibres from a particular individual which would only yield data based on

fewer individuals. Furthermore, from the forensic perspective, one must take into

account that crime scenes are not ideal, and the analyst may only be working with single

hair fibres or fragments of fibre. The research, however, does encompass the analysis

of single fibres from a vast number of individuals from many ethnic backgrounds to

construct a much broader database.

The objects that were classified in the PCA of the original spectral database (Figure 4.3)

were reassessed and labelled according to their reclassified chemical state based on the

FC results. Each individual contributed approximately 10-15 spectra from two to three

fibres. The fibres were labelled as untreated (blue), treated (pink), mildly treated

(green) or fuzzy objects (black).

The reclassified PCA scores plot is presented in Figure 4.4. It can be seen that with the

inclusion of the 116 „fuzzy‟ samples to the database, the objects are widely spread along

the PC1 axis and no discernible trends were found. Therefore, to simplify the scenario,

fuzzy objects bearing only those clear cut memberships in the three classes were

omitted from the database.

162

Figure 4.4 – Re-classified PCA scores plot of PC1 (74.8 %) vs. PC2 (14.4 %) of the

untreated fibres (blue), the chemically treated fibres (pink), the mild treated fibres

(green) and the „fuzzy‟ samples (black) of the Caucasian and Asian fibres.

The PCA scores of the spectral data matrix without the „fuzzy‟ samples are presented in

Figure 4.5. It is immediately apparent that untreated fibres (blue objects) with negative

scores are discriminated on PC1 from the chemically treated fibres (pink objects) with

positive scores on the same PC. Further separation of the spectral database can be

delineated along the PC2 axis which explains the next highest amount of spectral data

variance (14.4 %). The spectral objects from mildly treated hair fibres (green), cluster

tightly positive on PC2, and are separated from untreated and chemically objects, which

negative scores on PC2.

-30

-25

-20

-15

-10

-5

0

5

10

15

-50 -40 -30 -20 -10 0 10 20 30 40

PC1 (74.8%)

PC

2 (

14

.4%

)

Untreated Treated Mild Treatment Fuzzy Samples

163

Figure 4.5 – Re-classified PCA scores plot of PC1 (74.8 %) vs. PC2 (14.4 %) of the

untreated fibres (blue), the chemically treated fibres (pink) and the mildly treated fibres

(green) of the Caucasian and Asian hair fibres between 1750-800 cm-1

.

Also, the mildly treated group of objects is separated on PC1 into two groups, one with

spectra from fibres that have mild physical treatment (negative on PC1) and those that

have been exposed to mild chemical oxidation (positive on PC1) according to the “hair

history” records.

To explore the possible separation and sub-division of the above mildly treated group,

fuzzy clustering was repeated on the database using a four class model based on 4 PCs

(96 % total data variance). The FC membership values of the 4 clusters are presented in

Appendix III. This table supports the presence of a fourth group. With reference to the

typical untreated (blue) and chemically treated (pink) fibres (CFUN1 and CFTR10),

they display membership in Clusters 3 and 4, respectively. The mildly treated fibres are

segregated into classes‟ noted above „mild physical treatment‟ (turquoise) and „mild

chemical treatment‟ (green) which consist of spectral objects with membership in

Clusters 1 and 2 respectively. However, the calculation of a fourth cluster increased the

-30

-25

-20

-15

-10

-5

0

5

10

15

-50 -40 -30 -20 -10 0 10 20 30 40

PC1 (74.8%)

PC

2 (

14

.4%

)

Untreated Treated Mild Treatment

CFUN 1 CFTR 10

Untreated

Chemically Treated

Mildly Treated

164

number of „fuzzy‟ samples (white) from 116 to 132 or about 45 % of the Caucasian and

Asian database.

The PC1 versus PC2 scores plot based on the four class FC model is presented in Figure

4.6. It shows that the mildly treated group has been divided along the PC1 axis. The

spectral objects from fibres subjected to mild physical treatment (turquoise), adjacent to

the untreated group, have negative scores on PC1 and positive ones on PC2, and are

separated from the objects from fibres subjected to mild chemical treatment (light

green) (positive scores on PC1 and PC2). However, the main difference to the PCA

scores plot of the 3 class model is that the number of chemically treated fibres has

increased. This refers to the cluster of spectral objects on positive PC2, which appear to

segregate the physical and mild treated groups. The boundaries between the three

groups are indistinct, which increases the likelihood of misclassification. But

importantly it should be noted that this comparison between the FC modelling and the

2-dimensional representation should only be regarded as an approximation because the

FC modelling was carried out with information from 3 and 4 dimensional spaces i.e. 3

or 4PCs, rather than just 2.

Figure 4.6 – PCA scores plot of PC1 (74.8 %) vs. PC2 (14.4 %) of the untreated fibres

(blue), the chemically treated fibres (pink), the mildly physically treated fibres

(turquoise), and the mild chemically treated fibres (light green) of the Caucasian and

Asian hair fibres between 1750-800 cm-1

based on a four class FC model.

-30

-25

-20

-15

-10

-5

0

5

10

15

-50 -40 -30 -20 -10 0 10 20 30 40

PC1 (74.8%)

PC

2 (

14

.4%

)

Untreated Chemically Treated Mild Chemical Treatment Mild Physical Treatment

Untreated

Mild Physical Treatment

Mild Chemical Treatment

Chemically Treated

CFUN 1

CFTR 10

165

Nevertheless, the PCA plot (Figure 4.5) still reflects the observations seen in the FC

results which illustrate that a third class of hair fibre is apparently present. This

observation indicates that the original protocol design is inadequate (Figure 4.1)24

as it

considers only two classes: untreated or chemically treated. This suggests that on initial

inspection of the unknown or questioned fibre, spectral objects potentially could belong

to one of three classes (or possibly four) which relate to the chemical state of the fibre.

Hence, with this evidence, a third branch should be added to the tree diagram (Figure

4.1) which stems away from the unknown fibre to the third fibre type coined as mildly

treated.

Supporting the evidence for separation of the untreated from chemically treated spectral

objects is available in the PC1 loadings plot presented in Figure 4.7. Analysing the

positive loadings, which correlate to the scores of the chemically treated and

approximately half of the mildly treated fibres positive on PC1, it can be seen that these

spectral objects are most heavily influenced by the frequencies between 1200-1000 cm-1

(denoted in purple). Thus, the loadings plot supports the spectral evidence which

indicates that when a hair fibre is chemically treated, the products of the oxidation of

cystine are cysteic acid (1172 cm-1

, anti-symmetric stretch; and 1040 cm-1

, sym str.;

cystine dioxide (1121 cm-1

sym str.); and cystine monoxide (1071 cm-1

; sym str.).

166

Figure 4.7 – PC1 Loadings plot of the chemically treated and mildly treated fibres

(positive loadings), and the untreated and mildly treated fibres (negative loadings)

between 1750-800 cm-1

region.

Chemically treated fibres have spectra which are also consistently biased towards the

frequencies between 1750-1700 cm-1

(dark blue). This is attributed to the υ (C=O)

stretch of the COOH group. Previous IR and Raman spectroscopic investigations have

focused on the variations in amino acid composition in wool and hair as a consequence

of chemical treatments such as bleaching and permanent waving.184,236,290,299

Those

studies found that the aspartic and glutamic amino acids increased slightly (within a

magnitude of µmoles/gram) as a result of cosmetic treatment.

To a lesser extent, weak positive loadings indicate that treated hair fibres are also

influenced by frequencies between 1350-1265 cm-1

(dark green) which can be assigned

to the δ(CH2) deformation bending mode from the amino acid tryptophan at

1342 cm-1

.184

This bond has also been observed to increase slightly as a consequence of

treatment.269

The υs symmetric stretch of cystine dioxide (SO2) stretch at 1315 cm-1

,

and finally the vibrational stretches at 1284 cm-1

and 1257 cm-1

, which pertain to υ (C-

167

N) stretch and δ (N-H) in-plane-bend of the α-helix and random coil of the Amide III

band are also involved.

Conversely, the negative PC1 loadings which refer to the untreated fibres and

approximately half of the mildly treated fibres, are related to the frequencies between

1700-1350 cm-1

and 1260-1220 cm-1

which are attributed to the Amide I and Amide II

bands (black) at approximately 1627 cm-1

and 1515 cm-1

respectively. The deformation

and bending modes of the δ(C-H), (CH2) and (CH3) groups (blue) at approximately

1461 cm-1

, 1445 cm-1

and 1392 cm-1

respectively, and lastly, the Amide III band (black)

of the β-sheet at approximately 1238 cm-1

are also involved. The results of the negative

PC1 loadings suggest that the stable peptide linkage of the polypeptide backbone

remains relatively undamaged. The hairs from this group have not been subject to any

form of chemical treatment. However, with reference to approximately half of the

mildly treated group, these may have undergone some weak form of

mechanical/physical stress according to the „hair history‟.

Supporting evidence of the discrimination between mildly treated and untreated-

chemically treated hair fibre spectra is presented on the PC2 loadings plot (Figure 4.8).

The positive PC2 loadings, which are attributed to the scores of the mildly treated hair

fibre spectra, are heavily influenced by variables within the wavenumber region of

1500-1241 cm-1

. It includes the deformation and bending modes of the δ(C-H), (CH2)

and (CH3) groups (dark blue), υs symmetric cystine dioxide stretch (dark blue); and the

stretching frequencies which pertain to the Amide III υ (C-N) stretch and δ (N-H) in-

plane-bend of the α-helix and random coil(dark green). The separation is also partially

influenced by the cystine dioxide and cystine monoxide stretches within

1115-1050 cm-1

(light blue). The positive loadings suggest that cystine monoxide and

cystine dioxides are products of mild oxidation of the cystine bond as a result of weak

physical/chemical processes. These processes therefore attribute to the formation of

mildly treated or intermediate hair fibres.

168

Figure 4.8 – PC2 Loadings plot of the mildly treated hair fibres (positive loadings), and

the untreated and chemically treated fibres (negative loadings) between

1750-800 cm-1

.

Alternatively, the negative PC2 loadings are heavily correlated to the scores of the

chemically treated hair fibre spectra and are influenced strongly by the variables within

the 1240-1120 cm-1

and 1115-1050 cm-1

range (purple). These sections refer to the

Amide III of the β-pleated sheet and the asymmetric and symmetric cysteic acid

stretches respectively. The negative loadings highlight that these fibres have undergone

strong oxidation of the cystine bond, producing the final product cysteic acid.

Hence, for the protocol using the current spectral region between 1750-800 cm-1

,

exploratory PCA with the aid of FC highlighted the separation of untreated and

chemically treated FT-IR spectra along the PC1 axis. The separation is predominantly

based of the formation of cysteic acid and intermediates from the oxidation of the amino

cystine. However, it has been illustrated that there is some ambiguity between the two

groups based on the cystine oxidation region between 1200-1000 cm-1

which suggested

that a third spectral group exists. Mildly treated fibres are separated from untreated and

chemically treated fibres along the PC2 axis.

169

However, exploratory PCA and FC alone are not suitable indicators to identify the

relationships between the three groups as the SIRIUS software does not accommodate

performance ranking. PROMETHEE and GAIA (Section 2.7.4.1 and Section 2.7.4.2)

however are designed specifically for ranking and investigating scenarios concerned

with decision making.300

4.2.1.2 PROMETHEE and GAIA Analysis: 1750-800 cm-1

Spectral Range

Previous studies concerning the forensic analysis of hair fibres have utilised MCDM

methods to investigate the relationship and differences between human and various

animal keratin fibres based on their differences in molecular structure.24

However, with

reference to the proposed forensic protocol, no investigations have been carried out

making it a novel approach.

Hence, this chemometrics technique was applied to the proposed protocol. The spectral

objects for ranking were selected from:

i. untreated fibres which were minimally oxidised and formed a relatively tight

PCA cluster (Figure 4.5).

ii. mildly treated fibres.

iii. chemically treated fibres which showed high levels of cysteic acid and

formed a loose cluster (Figure 4.5).

GAIA analysis was performed to investigate the relationships between PC1 and PC2

from the previously evaluated analysis (Section 4.2.1.1) used as criteria.

The 176 x 2 matrix of the PC1 and PC2 scores from the hair fibre spectra were imported

into the commercially available Decision Lab 2000 Software301

for MCDM analysis.

Table 4.1 outlines the MCDM scenario which shows the assignment of the ranking

sense (maximise/minimise), choice of the preference functions, P (a, b), and the

associated threshold value, σ, for the two criteria.

170

Table 4.1 Data matrix for ranking of Untreated, Mildly Treated and Chemically

Treated Hair Fibre Spectra by PROMETHEE (3-Class Model)

Criterion PC1 PC2

Function Type Gaussian Gaussian

Minimised True True

p - -

q - -

σ 14.4263 6.8363

Unit (a.u.) (a.u.)

Weight 1.00 1.00

The rationale for the selection of various parameters is discussed below. As a necessity

of the PROMETHEE model, each criterion must be maximised or minimised. If a

criterion is maximised, this implies that the objects with high values are best performing

or conversely, if a criterion is minimised the best performing samples have low values.

For this scenario, the PC1 and PC2 criteria were minimised. This implies that the

PROMETHEE net ranking flow should be dominated by the untreated fibre spectral

objects which have negative scores on PC1 and low scores on PC2. This was followed

by the mildly treated and chemically treated spectral objects which have positive scores

on PC1. From the six preference functions available in Sirius, the Gaussian preference

function was selected for the PC1 and PC2 criteria. It was chosen because the PC1 and

PC2 scores are derived from the decomposition of the spectra and measurements at any

spectral point are normally distributed.302

The weighting for each criterion was set to 1.

The PROMETHEE II net ranking flow chart derived from the above model is illustrated

in Table 4.2. The φ values range was +0.813<φ<-0.932, and the groupings showed that

the untreated samples (Un) (blue), are the most preferred samples occupying

approximately the first 33 ranks ranging from φ = +0.81 - (+0.45). Within these ranks

are the 10 spectra from the typical untreated reference sample, CFUN1, i.e. CFUN1 -

CFUN110 which verify that the other objects around them are of similar type.

171

Rank Object Net φ Index

1 CF18 0.813

2 Un 0.81

3 Un 0.797

4 CF19 0.795

5 Un 0.764

6 CF10 0.749

7 Un 0.747

8 Un 0.704

9 Un 0.677

10 CF17 0.674

11 Un 0.671

12 Un 0.67

13 Un 0.659

14 Un 0.647

15 Un 0.639

16 Un 0.631

17 Un 0.625

18 Un 0.606

19 Un 0.606

20 Un 0.593

21 Un 0.588

22 Tr 0.535

23 Un 0.534

24 CF1 0.528

25 Tr 0.525

26 Tr 0.518

27 CF16 0.516

28 CF14 0.504

29 Un 0.498

30 Tr 0.477

31 CF13 0.473

32 CF12 0.46

33 CF15 0.447

34 MT 0.44

35 MT 0.421

36 MT 0.381

37 MT 0.381

38 MT 0.371

39 Tr 0.365

40 MT 0.364

41 Un 0.364

42 MT 0.358

43 MT 0.323

44 CF102 0.306

45 MT 0.306

46 CF103 0.306

47 CF1011 0.299

48 CF1010 0.295

49 MT 0.295

50 Tr 0.294

51 Un 0.288

52 MT 0.243

53 MT 0.243

54 Un 0.241

55 MT 0.211

Table 4.2 – PROMETHEE II Net Flows of the 1750 – 800 cm-1

Database


56 MT 0.209

57 Tr 0.177

58 Tr 0.16

59 MT 0.16

60 CF106 0.154

61 MT 0.154

62 MT 0.143

63 CF105 0.143

64 MT 0.1395

65 MT 0.131

66 MT 0.131

67 MT 0.127

68 MT 0.115

69 MT 0.098

70 MT 0.076

71 MT 0.076

72 MT 0.069

73 Tr 0.064

74 MT 0.059

75 MT 0.038

76 MT 0.038

77 MT 0.035

78 MT 0.029

79 Tr 0.029

80 MT 0.027

81 Tr 0.023

82 MT 0.019

83 MT 0.019

84 Tr 0.014

85 MT 0.005

86 Tr 0

87 MT -0.003

88 MT -0.021

89 MT -0.021

90 MT -0.027

91 MT -0.031

92 MT -0.035

93 MT -0.035

94 Tr -0.052

95 MT -0.052

96 MT -0.056

97 MT -0.058

98 MT -0.058

99 CF109 -0.061

100 MT -0.075

101 CF101 -0.09

102 MT -0.092

103 MT -0.115

104 CF107 -0.117

105 CF104 -0.135

106 MT -0.141

107 MT -0.146

108 MT -0.148

109 MT -0.152

110 MT -0.152


111 MT -0.162

112 Tr -0.162

113 MT -0.163

114 MT -0.174

115 MT -0.177

116 Tr -0.177

117 Tr -0.187

118 MT -0.198

119 Tr -0.205

120 MT -0.211

121 MT -0.211

122 MT -0.215

123 MT -0.216

124 MT -0.217

125 MT -0.249

126 CF108 -0.256

127 Un -0.265

128 MT -0.274

129 MT -0.281

130 MT -0.294

131 MT -0.294

132 MT -0.297

133 Tr -0.305

134 MT -0.314

135 Tr -0.323

136 Tr -0.327

137 MT -0.334

138 MT -0.334

139 MT -0.342

140 MT -0.362

141 MT -0.376

142 MT -0.387

143 MT -0.402

144 MT -0.405

145 MT -0.427

146 MT -0.431

147 MT -0.471

148 MT -0.471

149 MT -0.477

150 MT -0.48

151 MT -0.486

152 MT -0.499

153 MT -0.505

154 MT -0.507

155 MT -0.507

156 MT -0.519

157 MT -0.525

158 MT -0.555

159 MT -0.557

160 MT -0.575

161 MT -0.577

162 Tr -0.595

163 MT -0.615

164 MT -0.615

165 MT -0.637

172

Table4.2 - Contined

The mildly treated (MT) objects (green) dominate the middle and lower ranks from φ =

0.44-(-0.059) and φ = -0.14-(-0.72). Inter-dispersed within the mildly treated objects

are the chemically treated (TR) ones (pink) in the φ ranges of +0.31 – (+0.29) and φ = -

0.06-(-0.13) which are the typical treated reference spectra, CFTR10, i.e. CFTR101 –

CFTR1011. The high scattering amongst the mildly treated and treated objects is

attributed to the relatively high and non-uniform band intensity of the cysteic acid in

those fibre samples as compared to that present in the untreated fibres.

The GAIA bi-plot (Figure 4.9) for this matrix provides a display of the PC1 and PC2

criteria and the 176 spectral objects, decomposing the net outranking flows, providing

additional information to PROMETHEE II. In total, 100 % of the data variance is

accounted for by the first two PCs, hence all the information has been retained on the

GAIA plane. This bi-plot shows that the spectral objects can be separated into three

groups, with the untreated fibres forming a tight cluster with high scores on positive

PC1; the mildly treated fibres forming a moderate cluster on mainly negative PC1 and

positive PC2; and the chemically treated fibres spread across the PC1 axis and on

negative PC2. Hence, the plot demonstrates a similar distribution of the 176 spectra in

the PCA scores-scores plot of the three classes providing supporting evidence that three

classes of fibre exist.


166 MT -0.637

167 MT -0.697

168 MT -0.71

169 MT -0.72

170 MT -0.72

171 MT -0.723

172 MT -0.723

173 Tr -0.773

174 Tr -0.789

175 MT -0.811

176 Tr -0.932

Legend

Untreated (Un) = Blue

Mildly Treated (MT) = Green

Treated (Tr) = Pink

173

Δ 100 %

Figure 4.9 – GAIA analysis of the 176 spectra for the Caucasian and Asian hair fibre

database between 1750-800 cm-1

; ■ untreated fibres, ■ chemically treated fibres, ■

mildly treated hair fibres, ● pi (Π) decision-making axis, and ■ Original PC1 and PC2

criteria using a Gaussian preference function.

The two criteria vectors, PC1 and PC2 (dark green), are orthogonal to each other where

PC1 favours the better performing untreated hair spectra, and are separate from the

chemically treated objects which are favoured by the PC2 criterion. The Π decision axis

(red line) is very strong, indicating a robust decision, pointing towards the untreated

fibre spectral group.

PC1

Untreated

Chemically Treated

Mild Treatment

PC2

174

To explore the possible sub-division of the mildly treated objects into mild physical and

mild chemical classes, PROMETHEE II and GAIA was performed on the PCA scores

data (PC1 through to PC3, 96 % data variance) from Figure 4.6 (Table 4.3).

Table 4.3 Data matrix for ranking of Untreated, Mildly Treated and Chemically

Treated Hair Fibre Spectra (4-Class Model)

Criterion PC1 PC2 PC3

Function Type Gaussian Gaussian Gaussian

Minimised/Maximised Minimised Minimised Minimised

p - - -

q - - -

σ 14.6998 7.0119 3.7362

Unit (a.u.) (a.u.) (a.u.)

Weight 1.00 1.00 1.00

Table 4.4 represents the net flow PROMETHEE ranking chart of the 1750-800 cm-1

database based on a 4-class model. The φ values range was +0.831<φ<-0.64, illustrating

that the untreated (Un) samples (blue) are the most preferred samples occupying

approximately the first 27 ranks ranging from φ = +0.831 – (+0.393). The treated (TR)

objects are the next preferred samples, occupying ranks between φ = +0.366-(-0.006)

followed by the mild physical treated (MPT) objects between φ = +0.006 - (-0.042) and

φ = -0.206 – (-0.236). The mild chemical treated (MCT) samples are the least preferred

objects dominating the lower ranks from approximately φ = -0.33-(-0.42). The ranking

of the objects using a 4-class model suggests a trend for the relationship between four

hair classes, however the boundaries between each class are indefinite. The model does

favour the 3-class model as delineated by PCA (Figure 4.5) and GAIA (Figure 4.10),

showing three distinct groups. Hence, the evidence suggests that the 3-class model is

sufficient for discrimination and classification of hair fibre spectra.

175

Table 4.4 – PROMETHEE II Net Flows of the 1750 – 800 cm-1

Database (4 Class Model)


1 Un 0.831

2 CFUN17 0.814

3 Un 0.767

4 Un 0.747

5 CFUN18 0.743

6 Un 0.733

7 CFUN19 0.705

8 Un 0.698

9 Un 0.697

10 CFUN16 0.673

11 Un 0.634

12 Un 0.627

13 CFUN15 0.579

14 Un 0.548

15 Un 0.539

16 Un 0.528

17 CFUN12 0.518

18 Un 0.502

19 CFUN13 0.494

20 Tr 0.491

21 CFUN110 0.474

22 Un 0.464

23 Tr 0.437

24 CFTR102 0.41

25 Un 0.41

26 CFUN14 0.395

27 CFUN11 0.393

28 Tr 0.366

29 Un 0.361

30 Tr 0.335

31 Un 0.332

32 Tr 0.327

33 CFTR103 0.317

34 Un 0.317

35 Tr 0.295

36 Tr 0.287

37 Un 0.284

38 Tr 0.284

39 Un 0.282

40 Tr 0.267

41 CFTR1010 0.248

42 Tr 0.224

43 Tr 0.206

44 Tr 0.206

45 MCT 0.195

46 Tr 0.194

47 Tr 0.183

48 Tr 0.182

49 CFTR105 0.166

50 Tr 0.166

51 Tr 0.147

52 MPT 0.137

53 Tr 0.135

54 Tr 0.131

55 Tr 0.129


56 CFTR1011 0.125

57 MPT 0.121

58 MPT 0.097

59 Tr 0.086

60 CFTR107 0.076

61 MPT 0.069

62 Tr 0.062

63 Tr 0.058

64 Tr 0.058

65 Tr 0.054

66 Tr 0.050

67 Un 0.050

68 Tr 0.044

69 Tr 0.039

70 Tr 0.039

71 CFTR109 0.027

72 Tr 0.022

73 Tr 0.022

74 Tr 0.014

75 Tr 0.014

76 Tr 0.006

77 Tr 0.005

78 Tr 0.005

79 MPT 0.005

80 CFTR108 -0.000

81 MPT -0.003

82 Tr -0.004

83 CFTR104 -0.012

84 CFTR106 -0.012

85 MPT -0.022

86 MPT -0.032

87 MPT -0.035

88 MPT -0.041

89 Tr -0.042

90 Tr -0.046

91 MCT -0.058

92 Un -0.059

93 Tr -0.065

94 MPT -0.070

95 Tr -0.089

96 MPT -0.093

97 MPT -0.099

98 Tr -0.101

99 MCT -0.107

100 MPT -0.109

101 MPT -0.121

102 MCT -0.128

103 MCT -0.131

104 MCT -0.151

105 Tr -0.167

106 MPT -0.169

107 MCT -0.184

108 MPT -0.206

109 MPT -0.209

110 MPT -0.210

176

Table 4.4 - Continued


111 MPT -0.213

112 MPT -0.228

113 Tr -0.229

114 Tr -0.232

115 MPT -0.235

116 Tr -0.249

117 Un -0.253

118 MPT -0.263

119 MCT -0.273

120 Tr -0.276

121 Tr -0.281

122 MPT -0.289

123 MPT -0.306

124 Tr -0.317

125 Tr -0.320

126 Tr -0.329

127 MCT -0.330

128 MCT -0.331

129 MPT -0.335

130 MPT -0.341

131 MCT -0.355

132 MPT -0.356

133 MCT -0.357

134 MPT -0.361

135 MPT -0.363

136 MCT -0.365

137 MCT -0.372

138 Tr -0.374

139 MPT -0.374

140 MCT -0.382

141 Tr -0.394

142 Tr -0.400

143 MCT -0.401

144 MCT -0.409

145 MCT -0.418

146 MCT -0.418

147 Tr -0.425

148 MPT -0.427

149 Tr -0.443

150 MCT -0.446

151 MCT -0.45

152 MPT -0.459

153 Tr -0.467

154 Tr -0.491

155 MPT -0.491

156 MCT -0.510

157 MPT -0.517

158 MPT -0.547

159 Tr -0.564

160 Tr -0.567

161 Tr -0.581

162 Un -0.588

163 MCT -0.588

164 MPT -0.640

Legend



(MPT) = Turquoise

Mild Chemical Treatment

(MCT) = Green

Treated (Tr) = Pink

177

Δ 73.1 %

Figure 4.10 - GAIA analysis of the 164 spectra for the Caucasian and Asian hair fibre

database between 1750-800 cm-1

using a 4-cluster model; ▲untreated fibres, ■

chemically treated fibres, ■ mild chemical treatment hair fibres, ■ mild physical

treatment hair fibres, ● pi (Π) decision-making axis, and ■ PC, PC2 and PC3 criteria


Untreated


Mild Chemical Treatment Chemically Treated

PC2

PC1

178

4.2.1.3 Conclusions: 1750-800 cm-1

Database

The PC1 versus PC2 scores plot (Figure 4.3) showed a complicated scenario in which

many spectral objects were classified according to the historical record of the hair fibres

provided by the donors. These objects did not fall into the expected treated-untreated

classes.

Fuzzy clustering analysis using a three class model indicated the presence of a third

group and also some fuzzy objects. In total, 116 spectra (40 %) of 292 spectra

displayed fuzzy membership and could not be used for the spectral database. By

discarding those samples the robustness of the database is reduced, hence, the

separations of the three fibre classes are based on fewer samples.

When this fuzzy group was removed, the PCA plot also indicated a possible third group.

The PC2 loadings suggested that the group belonged to a mildly treated class, which

was generally characterised by much lower intensity cysteic acid bands. Furthermore,

the PC1 versus PC2 scores plot showed that the mildly treated group could be further

separated based on the historical record into the mild physical and mild chemical treated

groups. This conclusion is in reasonable agreement with the SEM observations, which

generally showed that hair fibres can be classified on a morphological basis, into three

groups, which reflected the level of fibre oxidation.

Fuzzy clustering using a four class model separated the mildly treated group into mild

physical treatment (e.g. from a combination of rigorous shampooing, towel drying,

combing, styling and surface treatments such as gel, wax, mousse etc.) and mild

chemical treatment (due to aging and photo-chemical oxidation). However, using PCA

and PROMETHEE, the boundaries between the four fibre classes were indefinite, due

to the non-uniform intensity of the cysteic acid vibrational band.

The PC1 loadings plot (Figure 4.7) also demonstrated that chemically and mildly treated

spectra are strongly influenced by the υa(C=O) stretch of the carboxylic acid group and

δ(O-H) bending vibration of H2O between 1750-1690 cm-1

because the of the increase

in intensity of the aspartic and glutamic acid vibrational bands, and the hydrophilic

nature of the fibre

179

Hence, alternative spectral ranges were investigated within the 1700-1200 cm-1

region.

Thus, excluded were the cystine oxidation region between 1200-800 cm-1

, the acidic

side chain residues and the carboxylic acid and water region between 1750-1690 cm-1

.

With the removal of these specific regions from the spectra, the major differences were

now attributed to the contributions of the different conformational forms - α-helix, β-

sheet and random coil.

Hence, the main data matrix of the keratin FTIR-ATR spectral database were pre-

processed into a number of sub-set data matrices, 1690-1200 cm-1

(Amide I, II and III),

and 1690-1500 cm-1

(Amide I and II). The 1690-1360 cm-1

(Amide I, II and δ(C-H)

deformation and bending) and second derivative spectral objects was also investigated

but it gave poor results. All the regions investigated are summarised in Table 4.9 and in

Appendices II, III, IV and X.

4.2.2 Investigation of the Alternative Spectral Regions

4.2.2.1 Spectral Range - 1690-1200 cm-1

The 1690-1200 cm-1

spectral region is exclusive to the vibrations of the Amide I – III

bands, and the δ(C-H), (CH2), (CH3) deformation and bending absorptions. The

previous 1750-800 cm-1

example illustrated that the PC scores for the spectral database

could not be designated according to the hair history because some fibres displayed

„fuzzy‟ membership between three classes of fibre. Hence, the 1690-1200 cm-1

spectral

database was submitted to FC for classification.

To segregate the spectra into the untreated, mildly treated and chemically treated

groups, a three-cluster model was calculated with a hard (p = 1.2) weighting exponent

based on 4 PCs which explained 98.76 % data variance. The FC membership value for

classes 1, 2 and 3 is presented in Appendix IV.

With reference to the typical untreated CFUN 1 spectral objects, the table illustrates that

untreated fibres (blue) display memberships values of 1 or close to 1 with a hard

exponent in column/class 2. The reference chemically treated fibre objects, CFTR 10,

180

(pink) are found in class 1 and this supports the view that other objects in that class are

either chemically or otherwise treated. This is confirmed by the initial classification of

the fibres. The third cluster, the mildly treated fibres (green), belongs to class 3. The

spectra in the table highlighted in red represent the „fuzzy‟ samples which have

membership in multiple classes.

In total, there were 77 spectral objects out of 212 which were fuzzy. This is

approximately 26 % of the total Asian and Caucasian spectral database. Hence, by

excluding the cystine oxidation spectral region, 39 less spectral objects exhibited fuzzy

membership as opposed to the 116 (fuzzy) spectra using the traditional 1750-800 cm-1

region.

The PCA scores-scores plot of the 1690-1200 cm-1

wavenumber region minus the

„fuzzy‟ samples is presented in Figure 4.11. In total, 87.8 % of the total spectral data

variance is explained by the first two PCs with 79.5 % variance on PC1 and 8.3 %

variance on PC2.


(blue), chemically treated fibres (pink), mildly treated fibres (green) using the alternate

spectral region between 1690-1200 cm-1

.

-10

-5

0

5

10

15

20

25

-40 -30 -20 -10 0 10 20 30

PC1 (79.5%)

PC

2 (

8.3

%)

Untreated Chemically Treated Mildly Treated

Increase in

Physical/Chemical

Treatment

Untreated

Chemically Treated

Mildly Treated

CFUN 1

CFTR 10

181

With the aid of the typical spectral references CFUN 1 and CFTR 10, it can be seen that

PC1 favours the separation of untreated (blue) and chemically treated (pink) hair fibres.

The mildly treated group forms a tight cluster with negative scores on PC2 and is more

or less separated along the same axis from the other two classes, demonstrating some

overlap with the chemically treated group. The mildly treated spectra that overlap with

the chemically treated spectra pertain to samples that have been subject to mild

chemical oxidation (i.e. photo-chemical oxidation) as opposed to damage by physical

processes which contribute to the majority of the mildly treated group.

The main difference between the PCA plots of the 1750-800 cm-1

and the

1690-1200 cm-1

spectral regions relates to the variance within untreated and treated

spectral groups. In the 1750-800 cm-1

plot, the untreated spectral group forms a very

tight cluster suggesting little variance between such samples, whereas in the

1690-1200 cm-1

region the untreated samples form a very loose cluster which illustrates

samples within the group are different.

The spectra of untreated spectral objects in the former region displayed little presence of

cysteic acid and the spectra were similar in contrast to the treated, however when the

cystine oxidation region was removed the main differences within the group are based

on the proteins conformation which appears to vary.

The opposite effect is seen with the chemically treated spectral objects which form a

very loose cluster in the 1750-800 cm-1

plot and a very tight cluster in the

1690-1200 cm-1

plot. The intensity of the cysteic acid and the associated intermediates

peaks varied for the chemically treated samples. This was dependent on the level of

chemical treatment, and hence there were significant spectral differences and more

spectral objects spread in the 1750-800 cm-1

plot. When the cystine oxidation region

was removed, the objects appear to have similar spectral band structure and hence form

tight clusters.

The spectral regions that separate the three classes of hair fibre within the

1690-1200 cm-1

are shown in the PC1 and PC2 loadings plots (Figure 4.12 and Figure

4.13). For the PC1 loadings, chemically treated fibres (positive loadings) are heavily

influenced by the vibrations of δ(C-H), (CH2), (CH3), (CH2)TRP, and the υs(C=O) stretch

182

of the carboxyl anion between approximately 1490-1310 cm-1

(green) and the Amide III

band between approximately 1310-1200 cm-1

(purple).

Untreated fibres are strongly influenced by the absorptions of the Amide I and Amide II

vibrational bands between approximately 1681-1490 cm-1

(black), again indicating that

untreated fibres represent stable peptide linkages.

Figure 4.12 - PC1 Loadings plot of the chemically treated fibres (positive loadings) and

the untreated and mildly treated fibres (negative loadings) between

1690-1200 cm-1

.

The PC2 loadings analysis demonstrates that the untreated and chemically treated

(positive loadings) samples are heavily influenced by the anti-symmetric υa(C=O)

carbonyl of the carboxyl anion and Tryptophan stretches between 1580-1500 cm-1

as

well as the deformation band of the δ(CH2) and (CH3) groups between approximately

1480-1440 cm-1

. To a lesser extent, such fibres are also influenced by the Amide II

band between 1550-1515 cm-1

and the deformation of δ(CH2)TRP of the tryptophan

residue and the symmetric υs(C=O) stretch of the carboxyl anion.

183

The negative loadings, which are attributed to the mildly treated fibres, are influenced

by the stretches of the β-sheet, random coil and α-helix modes of vibration of the Amide

I band between approximately 1690-1590 cm-1

and the Amide III band between

1315-1200 cm-1

.

Figure 4.13 – PC2 Loadings of the untreated and chemically treated fibres (positive

loadings) and mildly treated fibres (negative loadings) between 1690 -1200 cm-1

.

184

To investigate the relationship between the three groups, the 212 x 2 matrix of the PC1

and PC2 scores from the hair fibre spectra were submitted to an MCDM analysis. Table

4.5 outlines the MCDM modelling showing the assignment of the ranking sense,

preference function, P (a, b), and the associated threshold values for the two criteria.

Table 4.5 1690-1200 cm-1


and Chemically Treated Hair Fibre Spectra by PROMETHEE II

Criterion PC1 PC2


Minimised / Minimised Minimised Maximised

p - -

q - -

σ 9.6782 3.6142

Unit (a.u.) (a.u.)

Weight 1.00 1.00

As per the previous model, the data required for the PROMETHEE model is the same.

The PROMETHEE II net ranking flow φ indices are given in Table 4.6. The outflow

order, φ, was +0.93<φ<-0.62 which highlights that the untreated hair fibres are the most

preferred samples occupying the first 28 ranks ranging from φ = +0.93 – (+0.51).

The mildly treated samples (green) are the second most preferred samples which occupy

rankings between φ = +0.46 - (-0.27) inter-dispersed amongst approximately 1/3 of the

treated objects. The treated objects are the least preferred objects dominating the lower

ranks from φ = -0.33 – (-0.57). The main difference between the 1750-800 cm-1

and

1690-1200 cm-1

PROMETHEE II flow charts is that by excluding the cysteic acid

region, the treated group became more defined than scattered (as with the

1750-800 cm-1

region).

185

Table 4.6 - PROMETHEE II Net Flows of the 1690 – 1200 cm-1

Database


1 CF18 0.928

2 Un 0.928

3 CF19 0.904

4 Un 0.902

5 Un 0.889

6 CF110 0.883

7 CF17 0.879

8 Un 0.863

9 Un 0.844

10 Un 0.835

11 Un 0.817

12 CF13 0.816

13 CF16 0.801

14 Un 0.793

15 Un 0.787

16 CF11 0.770

17 CF14 0.759

18 CF15 0.753

19 Un 0.745

20 Un 0.713

21 Un 0.692

22 Un 0.687

23 Un 0.686

24 Un 0.679

25 Un 0.646

26 CF12 0.639

27 Tr 0.604

28 Un 0.512

29 MT 0.460

30 MT 0.457

31 MT 0.450

32 Tr 0.45

33 MT 0.372

34 Un 0.363

35 MT 0.354

36 Un 0.330

37 MT 0.320

38 MT 0.317

39 MT 0.310

40 Tr 0.284

41 Tr 0.274

42 MT 0.265

43 MT 0.264

44 MT 0.259

45 Tr 0.250

46 Tr 0.249

47 CF103 0.236

48 MT 0.234

49 Tr 0.231

50 CF102 0.225

51 Un 0.223

52 MT 0.216

53 Tr 0.195

54 Un 0.191


55 MT 0.181

56 Un 0.176

57 Un 0.167

58 MT 0.163

59 Un 0.159

60 MT 0.157

61 MT 0.150

62 MT 0.117

63 Tr 0.110

64 MT 0.108

65 Tr 0.106

66 MT 0.104

67 MT 0.097

68 MT 0.097

69 Tr 0.089

70 MT 0.088

71 MT 0.083

72 CF105 0.065

73 MT 0.064

74 MT 0.054

75 Tr 0.041

76 MT 0.039

77 MT 0.033

78 MT 0.027

79 MT 0.026

80 Un 0.025

81 MT 0.024

82 MT 0.023

83 MT 0.014

84 MT 0.01

85 MT 0.006

86 Tr -0.009

87 MT -0.015

88 MT -0.017

89 MT -0.017

90 MT -0.020

91 MT -0.025

92 CF101 -0.026

93 MT -0.036

94 CF106 -0.036

95 Un -0.054

96 Tr -0.056

97 MT -0.059

98 MT -0.061

99 MT -0.061

100 MT -0.063

101 CF104 -0.064

102 Tr -0.100

103 MT -0.103

104 Un -0.106

105 MT -0.106

106 MT -0.119

107 MT -0.123

108 MT -0.123

109 MT -0.125

Legend



Treated (Tr) = Pink

186


110 MT -0.134

111 CF1011 -0.134

112 CF1010 -0.137

113 MT -0.140

114 MT -0.146

115 MT -0.149

116 MT -0.153

117 Un -0.154

118 Tr -0.163

119 MT -0.163

120 Tr -0.164

121 Tr -0.167

122 MT -0.177

123 MT -0.180

124 MT -0.182

125 Tr -0.183

126 MT -0.186

127 MT -0.194

128 Tr -0.195

129 Tr -0.201

130 MT -0.203

131 Tr -0.204

132 MT -0.204

133 Tr -0.204

134 Tr -0.207

135 MT -0.208

136 MT -0.210

137 MT -0.215

138 MT -0.216

139 MT -0.224

140 Tr -0.225

141 Tr -0.22

142 MT -0.237

143 Tr -0.239

144 MT -0.242

145 MT -0.243

146 Tr -0.246

147 MT -0.248

148 Un -0.249

149 Tr -0.250

150 MT -0.252

151 MT -0.255

152 MT -0.269

153 MT -0.269

154 MT -0.272

155 CF108 -0.282

156 MT -0.283

157 Tr -0.286

158 Tr -0.287

159 Tr -0.288

160 Tr -0.292

161 MT -0.299

162 MT -0.301

163 MT -0.304

164 MT -0.304


165 Tr -0.31

166 Tr -0.310

167 Tr -0.311

168 MT -0.311

169 Tr -0.312

170 MT -0.314

171 Tr -0.315

172 MT -0.316

173 MT -0.316

174 Tr -0.318

175 MT -0.323

176 MT -0.325

177 MT -0.325

178 MT -0.326

179 Tr -0.327

180 MT -0.330

181 CF108 -0.335

182 MT -0.336

183 Tr -0.344

184 MT -0.346

185 Tr -0.351

186 MT -0.359

187 MT -0.361

188 MT -0.364

189 Tr -0.364

190 MT -0.366

191 MT -0.367

192 Tr -0.368

193 Tr -0.369

194 MT -0.377

195 CF107 -0.396

196 Tr -0.402

197 Tr -0.405

198 Tr -0.418

199 Tr -0.420

200 Tr -0.433

201 Tr -0.437

202 Tr -0.458

203 MT -0.459

204 Tr -0.465

205 MT -0.467

206 Tr -0.472

207 Tr -0.478

208 MT -0.495

209 Tr -0.505

210 MT -0.546

211 Tr -0.572

212 MT -0.62


187

The GAIA bi-plot of the criteria and the 212 spectra for the 1690-1200 cm-1

matrix is

presented in Figure 4.14. In total, 100 % of the data variance is accounted for by the

first two PCs, hence all the information is retained. From the plot, one is able to

conclude that spectral objects are roughly separated into three groups. However, the

majority of the mildly treated spectra form a tight cluster with scores on positive PC1

but clearly disperse across the PC1 axis and integrate with the chemically treated

spectra which form a tight cluster on negative PC1. The untreated fibres form a tight

cluster on both positive PC1 and PC2. This group is relatively separate from the other

two groups which further illustrated their difference in chemical structure.

The two criteria vectors, PC1 and PC2 (dark green), are orthogonal and moderately

surround the majority of the mildly treated hair spectra. The Π decision axis (red line)

is very strong, indicating a robust decision, pointing towards the mildly treated fibre

spectral group with minor influence from some untreated and chemically treated

spectra. Hence, for this matrix, the mildly treated fibres are the better performing

samples.

In comparison to the GAIA plot the 1750-800 cm-1

matrix (Figure 4.10), there is more

overlap between the mildly and chemically treated groups which creates a grey area for

the discrimination between those fibre types. However, the main difference between

the two GAIA plots concerns the decision axis vector which favours the mildly treated

fibres over the untreated fibres.

188

Δ 100 %

Figure 4.14 - GAIA analysis of the 212 spectra for the 1690-1200 cm-1

hair fibre

database; ▲ untreated fibres, ■ chemically treated fibres, ■ mildly treated hair fibres,

● pi (Π) decision-making axis, and ■ PC1 and PC2 criterion variables using a


For the protocol, using the 1690-1200 cm-1

spectral region, having the mildly treated

fibres as the stronger performing samples is not feasible. The PCA, PROMETHEE and

GAIA evidence demonstrate that the group is not isolated because the spectra share

similar characteristics to chemically treated fibres. However, the evidence illustrates

that the untreated fibres are an isolated group which represent spectra in the “raw”

chemical state and thus should be used as the reference set.

Mild Treatment

Chemically Treated

Untreated

PC2

PC1

189

The loadings analysis for the 1690-1200 cm-1

region revealed that the Amide III band

(β-pleated sheet), affects the separation between treated and untreated fibres. The IR

evidence in the previous chapter (Section 3.2.3) illustrated that the band slightly

increases with chemical treatment due to an increase in the random coil conformation.

Therefore, the assessment of the next alternative spectral region excluded the Amide III

band from the spectrum.

4.2.2.2 Chemometric Analysis of Single Human Hair Fibres using Alternative Spectral

Regions - 1690-1500 cm-1

The 1690-1500 cm-1

IR region for hair keratin is restricted only to the vibrations of the

Amide I and Amide II bands. FC analysis of a 3-cluster model with hard weighting (p =

1.2) was performed on the 292 spectra. The FC results for the 1690-1500 cm-1

database

are presented in Appendix V. The untreated (CFUN1) and chemically treated

(CFTR10) reference spectra illustrate that those classes show membership to clusters

two and one respectively. The mildly treated class shows membership to class three.

The samples highlighted in red have fuzzy membership. In total, 83 spectra had fuzzy

membership; a loss only 28 % of the total database which is an improvement in

comparison to the current 1750-800 cm-1

analysis region.

The PCA scores-scores plot of the 1690-1500 cm-1

wavenumber region is presented in

Figure 4.15. In total, 88.9 % of the total spectral data variance is explained by the first

two PCs with 72.3 % variance on PC1 and 16.6 % variance on PC2 (4 PCs 97 % data

variance). It can be seen that with the exclusion of the amino acid side chain

contribution from the spectrum, the total % data variance is similar to the data variance

explained by the 1700-850 cm-1

PCA plot (89.2 %).

190


(blue), mildly treated fibres (green) and the chemically treated fibres (pink) using the


.

With respect to the reference samples (CFUN 1 and CFTR 10), the untreated spectra

(blue) form a loose cluster with positive scores on PC1 and PC2 and are separated

across the PC1 axis from the chemically treated spectra which form a tight cluster with

negative scores on PC1. The mildly treated fibres form a tight cluster with negative

scores on PC2, adjacent to the chemically treated and untreated group. The overlap of

scores between the mildly treated and chemically treated groups is low, compared to the

1690-1200 cm-1

and 1690-1360 cm-1

PCA scores plots. The lack or reduction in overlap

is important because it decreases the likelihood of object misclassification. This is

especially important for classifying fibres of unknown origin.

The keratin spectra had been truncated to about 200 cm-1

, and the bands responsible for

the discrimination of untreated and treated fibres within 1690-1500 cm-1

are reflected in

the loadings plots (Figures 4.16 and 4.17).

-10

-5

0

5

10

15

20

-20 -15 -10 -5 0 5 10 15 20

PC1 (72.3 %)

PC

2 (

16

.6%

)

Untreated Treated Mildly Treated

Increase in

Physical/Chemical Treatment

CFUN 1

CFTR 10

Untreated

Mildly Treated

Chemically Treated

191

Figure 4.16 - PC1 Loadings plot of the untreated and mildly treated fibres (positive

loadings) and the chemically treated fibres (negative loadings) between

1690-1500 cm-1

.

Figure 4.17 - PC2 Loadings plot of the untreated and chemically treated fibres

(positive loadings) and the mildly treated fibres (negative loadings) between

1690-1500 cm-1

.

192

For the PC1 loadings (Figure 4.16), it can be seen that the untreated and mildly treated

fibres (positive loadings) are influenced by the α-helical and β-pleated sheet of the

Amide I and Amide II bands (black) between 1660-1600 cm-1

and 1550-1500 cm-1

respectively. Conversely, the treated fibres are influenced by the changes occurring to

the Amide I υ(CONH2) stretch of the asparagine and glutamine side chains and υ(C=O)

stretch of the β-pleated sheet and random coil conformation between approximately

1690-1670 cm-1

(dark blue); the anti-symmetric υa(C=O) carbonyl stretch of the aspartic

and glutamic acid between 1590-1570 cm-1

(green); and the vibration of the 3-

substituted indole ring of tryptophan between 1570-1550 cm-1

(blue).

The PC2 loadings (Figure 4.17) are complex because the positive loadings represent the

untreated fibres and approximately half of the chemically treated spectral group whereas

the negative loadings represent the mildly treated fibres and the other half of the

chemically treated fibres.

To investigate the relationship and ranking between the three groups, the 209 x 2 matrix

of the PC1 and PC2 scores from the hair fibre spectra was submitted to an MCDM

analysis. Table 4.7 outlines the MCDM scenario showing the assignment of the ranking

sense, preference function, P (a, b), and associated threshold values for the two criteria.

Table 4.7 1690-1500 cm-1

Data matrix required for ranking of Untreated, Mildly

Treated and Chemically Treated Hair Fibre Spectra by PROMETHEE (3-Class)

Criterion PC1 PC2


Maximised True True

p - -

q - -

σ 6.1065 3.0013

Unit (a.u.) (a.u.)

Weight 1.00 1.00

193

Maximisation of the PC1 and PC2 criteria for this scenario suggests that the

PROMETHEE net ranking flow should be dominated by the untreated fibres group

which have positive scores on PC1 and PC2 (best-performing samples) followed by the

mildly treated and chemically treated fibres (worst-performing samples).

The PROMETHEE II net ranking chart for the 1690-1500 cm-1

region is presented in

Table 4.8. The Φ values range was +0.95<φ<-0.57, which demonstrated that the

untreated samples are the most preferred samples occupying the first 27 ranks from φ =

0.95 – 0.47. The mildly treated and chemically treated fibres are the next preferred

samples which are well dispersed across the remaining 170 ranks between φ = 0.37 – (-

0.57).

The GAIA bi-plot of the 209 spectra for the 1690-1500 cm-1

database is presented in

Figure 4.18. In total, 100 % of the data variance is accounted for by the first two PCs.

The untreated fibres form a very tight cluster on +PC1 and –PC2, which is well

separated from the mildly treated and chemically treated fibres. The mildly treated

fibres form a dense cluster on +PC1 separated across the PC1 axis from the chemically

treated fibres forming cluster on –PC1. However, some overlap exists between the

mildly treated and chemically treated groups because of the close relationship in

conformation.

194

Table 4.8 - PROMETHEE II Net Flows of the 1690 – 1500 cm-1

Database


1 Un 0.949

2 Un 0.931

3 CF18 0.914

4 Un 0.905

5 Un 0.893

6 Un 0.887

7 CF19 0.884

8 Un 0.875

9 Un 0.872

10 Un 0.859

11 CF110 0.857

12 CF17 0.832

13 Un 0.811

14 Un 0.805

15 Un 0.781

16 Un 0.781

17 CF16 0.773

18 CF13 0.743

19 Un 0.698

20 CF14 0.688

21 CF11 0.681

22 CF15 0.649

23 CF12 0.6318

24 Tr 0.546

25 Un 0.501

26 MT 0.475

27 Un 0.47

28 MT 0.377

29 MT 0.376

30 MT 0.357

31 MT 0.305

32 Tr 0.304

33 MT 0.301

34 Tr 0.285

35 CF102 0.262

36 Tr 0.258

37 MT 0.247

38 Tr 0.241

39 Tr 0.239

40 MT 0.23

41 MT 0.223

42 MT 0.217

43 MT 0.215

44 Tr 0.206

45 MT 0.202

46 MT 0.17

47 Tr 0.164

48 MT 0.163

49 Tr 0.141

50 MT 0.135

51 CF103 0.132

52 MT 0.132

53 Tr 0.131

54 MT 0.129

55 Tr 0.123


56 CF105 0.123

57 MT 0.121

58 Un 0.118

59 MT 0.115

60 MT 0.115

61 MT 0.114

62 MT 0.103

63 Un 0.099

64 MT 0.097

65 MT 0.096

66 MT 0.094

67 Tr 0.084

68 MT 0.063

69 MT 0.061

70 MT 0.046

71 MT 0.045

72 MT 0.037

73 Tr 0.037

74 MT 0.032

75 MT 0.019

76 MT 0.015

77 Tr 0.013

78 MT 0.008

79 MT 0.003

80 Tr 0.001

81 Tr 0

82 MT -0.007

83 CF1010 -0.024

84 MT -0.027

85 MT -0.03

86 MT -0.033

87 CF106 -0.034

88 MT -0.036

89 MT -0.04

90 MT -0.054

91 CF104 -0.056

92 CF106 -0.056

93 MT -0.06

94 Tr -0.063

95 Tr -0.064

96 MT -0.065

97 MT -0.065

98 MT -0.066

99 MT -0.066

100 MT -0.068

101 MT -0.068

102 MT -0.071

103 Tr -0.071

104 MT -0.076

105 MT -0.076

106 MT -0.079

107 MT -0.09

108 MT -0.093

109 MT -0.096

110 MT -0.097

Legend



Treated (Tr) = Pink

195


111 MT -0.107

112 Tr -0.107

113 Tr -0.11

114 MT -0.111

115 MT -0.111

116 MT -0.119

117 MT -0.123

118 MT -0.134

119 CF109 -0.137

120 MT -0.139

121 MT -0.144

122 MT -0.147

123 MT -0.148

124 Tr -0.152

125 MT -0.155

126 MT -0.159

127 MT -0.163

128 MT -0.165

129 Tr -0.172

130 Tr -0.175

131 Tr -0.176

132 CF1011 -0.179

133 CF102 -0.18

134 Tr -0.182

135 Tr -0.182

136 Tr -0.183

137 MT -0.185

138 MT -0.186

139 MT -0.187

140 MT -0.19

141 MT -0.191

142 MT -0.191

143 Tr -0.196

144 CF107 -0.198

145 Tr -0.2

146 MT -0.2

147 Tr -0.201

148 MT -0.205

149 Tr -0.207

150 Tr -0.212

151 Tr -0.221

152 MT -0.222

153 Tr -0.229

154 Tr -0.231

155 MT -0.237

156 Tr -0.247

157 MT -0.251

158 Tr -0.256

159 Tr -0.258

160 Tr -0.26

161 MT -0.261

162 CF108 -0.261

163 MT -0.263

164 Tr -0.268

165 MT -0.272


166 Tr -0.277

167 MT -0.279

168 MT -0.28

169 MT -0.285

170 MT -0.289

171 Tr -0.296

172 MT -0.297

173 Tr -0.302

174 MT -0.315

175 Tr -0.318

176 Tr -0.319

177 Tr -0.323

178 MT -0.323

179 Tr -0.324

180 Tr -0.324

181 Tr -0.324

182 Tr -0.332

183 Tr -0.338

184 Tr -0.342

185 MT -0.345

186 MT -0.362

187 MT -0.381

188 Tr -0.384

189 Tr -0.389

190 MT -0.391

191 Tr -0.393

192 Tr -0.394

193 MT -0.397

194 MT -0.405

195 Tr -0.42

196 MT -0.425

197 MT -0.425

198 Tr -0.431

199 Tr -0.435

200 Tr -0.436

201 Tr -0.439

202 MT -0.445

203 MT -0.458

204 Tr -0.461

205 MT -0.512

206 Tr -0.523

207 Tr -0.547

208 MT -0.567


196

Δ 100 %

Figure 4.18 - GAIA analysis of the 208 spectra for the 1690-1500 cm-1

hair fibre




PC2

PC1

Mild Treatment

Chemically Treated

Untreated

197

4.2.3 Chemometric Analysis of Further Alternative Spectral Regions of Keratin

FTIR-ATR and Second Derivative Spectra

The 292 spectra of the Caucasian and Asian fibres were converted into second derivate

spectra as outlined in Section 2.5.1. The double-centred second derivative matrix was

then submitted to Sirius for chemometric analysis of the current and alternative spectral

regions. However, it must be taken into account that by taking the second derivative of

the spectra, the downwards peaks or troughs are related to the keratin spectrum and the

upwards peaks do not apply to the separation. Nevertheless, it was important to

consider whether second derivate spectra enhanced the separation of the three classes of

hair fibre. The results using second derivative did not enhance/improve the

discrimination of the spectral objects. The chemometric analyses of the alternative

regions are presented in Appendix X.


In summary, this chapter has dealt with a detailed study to determine the optimum

spectral conditions in which to investigate single human hair fibres as part of a forensic

protocol. The analysis used raw spectra, and for the first time second derivative spectra

were trialled. In preparation of the optimised protocol, some information came to light

that had not been discovered by previous investigations and is summarised below:

The historical record cannot be used for classification because of the vague

discrimination of an untreated and chemically treated fibre.

FC for unsupervised, non-biased classification was applied to the database to

determine how many classes of fibre were present.

3 types of hair fibre exist – untreated, mildly treated and chemically treated.

The mildly treated fibres exhibit an intermediate level of cystine oxidation.

The mildly treated group can be sub-divided into the mild physical and mild

chemical treated groups.

PROMETHEE II rank orders the objects from untreated, moderate to harsh

oxidation.

198

The GAIA bi-plot illustrates the clustering of the groups and indicates the

most preferred samples in the database.

Second derivative spectra are useful for qualitative analysis; however,

Chemometric analysis does not provide evidence for the basis of the

separations as loadings (variables) plot are complex.

After exploration of the traditional and several alternate spectral regions of the keratin

spectrum, the 1690-1500 cm-1

region (raw spectra) provided satisfactory results for

discrimination based on the robustness (No. of objects used), and the PCA and GAIA

separations. The results for the investigation of the protocol are summarised in Table

4.9.

199

Table 4.9 Summary of Chemometric Results for Current and Alternative Spectral

Regions of Raw and Second Derivative Spectra

Spectral Region

(cm-1

)

PCA

(No. of non-

fuzzy

Objects)

PCA

Separation*

(No overlap

to Heavily

Overlapped)

PROMETHEE

(Best Performing

Samples)

GAIA

Separation*

(No Overlap

to Heavily

Overlapped)

1750-800 cm-1

(3-Class Model)

176/292

60 %

Good Untreated Average-

Good

1750-800 cm-1

(4-Class Model)

164/292

56.2 %


Good

1690-1200 cm-1

(3-Class Model)

212/292

72.6 %

Average Mildly Treated Poor-Average

1690-1360 cm-1

(3-Class Model)

(Appendix X)

202/292

69 %

Average Untreated Average

1690-1500 cm-1

(3-Class Model)

209/292

72.0 %


Good

1750-800 cm-1

Second

Derivative

(3-Class Model)

(Appendix X)

176/292

60 %

Good Mildly Treated Average-

Good

1690-1500cm-1

Second

Derivative

(3-Class Model)

(Appendix X)

200/292

68.5 %

Average Untreated/Mildly

Treated

Poor-Average

* PCA and GAIA Separation = The evaluation in the table above is only a subjective

visual analysis method based on the effectiveness of the separation of the three fibre

groups untreated, mildly treated and chemically treated. For example, in the first

200

scenario, the PCA plot of 1750-800 cm-1

revealed that each group was separated by the

PC1 (mildly treated from untreated and chemically treated) and PC2 axis (untreated

from chemically treated) with very little overlap. However, the PCA plot of 1690-1200

cm-1

it can be seen that there is a lot of overlap between the mildly treated and

chemically treated groups which increases the risk of misclassification for unknown

spectra in that particular area. In the new, alternate region, 1690-1500 cm-1

, it provides

good separation of the three hair classes and less fuzzy objects are encountered.

201

5.0 APPLICATIONS OF THE FORENSIC PROTOCOL AS AN

IDENTIFICATION PROCEDURE FOR SINGLE HUMAN HAIR

FIBRES

5.1 Principles of the Forensic Protocol

Panayiotou24

envisaged that the protocol would be utilised by forensic authorities as a

procedure for the identification of questioned hair fibres that would corroborate the

information obtained from microscopic and genetic examinations. The „Blue Sky

Vision‟ of the ongoing research and development in this field is to create a

comprehensive database of hair fibre spectra to be utilised for comparison of hair fibres

of unknown origin. The database should encompass IR spectra from many different

types of hair sample to compensate for age; race/mixed race; grooming habits; cosmetic

desires; and personal lifestyle (i.e. swimming and tanning). Additionally, the database

should incorporate information regarding the whole hair fibre, which has been shown to

be different from root to tip.22

26

The information that would be extracted with aid of

this protocol should be employed for initial screening to narrow the scope and direction

of the forensic investigation.

However, the main disadvantage of Panyiotou‟s protocol was that it was limited to

Caucasian and Asian hair only and did not include the third important African-type

group. Also, it did not consider the possibility of sub-classes other than untreated and

treated hair e.g. light or heavily treated hair.

In the light of the above two disadvantages, Barton‟s work (2004) is significant. 23

He

collected an FTIR-ATR spectral database from a wide array of individuals and included

for the first time African-type hair fibres. The spectra were processed by PCA to

establish if the separations that were observed with the Asian and Caucasian fibres with

the use of FTIR-Micro-spectroscopy in the earlier Panayiotou study, were valid.24

As a

result, it was determined that with the introduction of African-type hair fibres, the

separation of those hairs on the basis of chemical treatment (i.e. the first separation of

202

the spectra as proposed by the protocol) appeared to contradict the initial protocol

model (Figure 4.2, Chapter 4, Section 4.2.1.1).

Interestingly, the PCA scores plot illustrated that some untreated African-type spectra

clustered with the chemically treated spectra with positive PC1 scores. Chemically

treated African-type hair spectra were observed to be associated with the untreated fibre

spectra with negative PC1 scores. No plausible results or evidence existed at the time to

explain these observations. However, at that stage, it was suggested that the

phenomenon could be explained through an understanding of the morphology and

chemical composition of the African-type hair fibre e.g. African hair fibres

characteristically have more crimp compared to the other races.303

With the PCA of the untreated African-type fibres and with reference to the “hair

history”, there was no evidence to suggest that these fibres could be considered outliers

or rather chemically treated fibres. The fibres had not been subjected to any hair

product/s, received minimal sun exposure and the individuals swam only rarely. Thus,

it was hypothesised that African-type hair fibres have elevated levels of cystine and

moderate to high levels of cysteic acid in comparison to the Caucasian and Asian races.

Consequently, any form of light to moderate natural weathering (i.e. photo-oxidative

bleaching) increases the concentration levels of cysteic acid in the hair fibre and when

processed by chemometrics, a spectrum from such a fibre would be recognised as that

of a treated fibre.

The treated African-type hair fibres also displayed atypical results at the time. The

main reason suggested for this behaviour was the use of surface treatments such as gel

and hairspray. It was also reasonable to suggest that there was further discrimination of

the treated African-type spectral objects from the other treated spectral objects (i.e.

Asian and Caucasian) on the basis of multiple treatments versus single treatment, which

had been indicated by Panayiotou in previous investigations.22

Thus, this complex issue was explored further in this work so as to define

analytical methodology, which would resolve the problem observed. Therefore,

this final chapter explores the strength and potential of the optimised forensic

protocol (Chapter 4) as a technique to differentiate between the structural

203

characteristics of single human hair fibres, which relate to chemical treatment,

gender and race.

The aims were:

In general, to analyse thoroughly the similarities and differences between FTIR-

ATR spectra of Asian, Caucasian and African-type human hair with the ultimate

aim of proposing a protocol, which could be applied in forensic investigations.

Specifically to:

1. Analyse Chemically Treated Hair Fibres

To study various chemically treated hair fibres from mild

chemical treatment (i.e. cosmetic surface treatments such as gel

and hairspray, straightening with an iron, etc.) to harsh oxidative

chemical treatments (i.e. bleaching and permanent dyeing).

2. Understand the Structural Differences between Hairs of

Different Gender Sources

To investigate the basis of separation between male and female

hair fibre spectra with supporting evidence from second

derivative and IR difference spectra (Section 3.2.2.1).

3. Investigate the Structural Differences of Hair from Subjects

of Different Race

To investigate the IR spectral variables that are significant in the

discrimination of hairs of each of the three major races, Asian,

Caucasian and African-type.

204

5.2 African-type Hair Fibres

Racial differences in scalp hair have been the subject of much interest.112

The term

African-type, refers to a major human racial classification traditionally distinguished by

physical characteristics. Black African-type hair, from the indigenous people of mainly

southern African (sub-Saharan Africa), Melanesia and Papua New Guinea (PNG)

region, is characterised by the tight spring-like coiling of the hair shaft.112

Additionally,

there are varying degrees of curl, and it has been hypothesised that these geometric

differences can influence the mechanical properties of hair.304

There are six main

physical and chemical attributes of African-type hair that separate them from Asian and

Caucasian hair fibres.

5.2.1 Physical and Chemical characteristics of African-type hair fibres:

1. Diameter and Cross-section: African-type hairs demonstrate a

high degree of irregularity in diameter and have an elliptical

cross-section.114

The diameter is smaller than that of the other

two races.91

2. Shape: The shape of a hair fibre resembles a twisted oval rod.114

3. Mechanical Properties: The hair has low tensile strength and

breaks more easily than Caucasian hair.114

Porter et al.304

suggest

that as the hair becomes more curly, it has a smaller curve

diameter, extends less when strained and is more susceptible to

breakage. It also has a tendency to form longitudinal fissures and

splits along the hair shaft.112

SEM studies have highlighted that

the majority of the tips had more fractured ends compared with

Asian and Caucasian hairs.112

Similarly, the basal end often

exhibited evidence of breakage in contrast to the Asian and

Caucasian samples in which the majority of hair had attached

roots.

4. Combing Ability: The hair is difficult to comb because of its

very curly configuration.114

The physical effect of washing,

drying and combing may increase knotting (Figure 3.4) and

205

intertwining by stretching out the coils, which then interlock

when they spring back.112

5. Chemical Composition: There are no significant differences in

the amino acid composition of hair of different ethnicity.114

6. Hair Moisture: African-type hair has less moisture content than

Caucasian and Asian hair, and thus, has a tendency to become dry

and brittle.114

The cause of the geometry of African-type hair is unknown.32

However, the results of

examination of scalp biopsies taken from African Americans indicate that highly curled

hair follicles may be a strong contributing factor. In summary, Khumalo et.al claim that

African-type hair is less fragile compared to that from the other races.32

In their study,

TEM micrographs of hair from African, Caucasian, Asian origins and persons with that

suffered from trichorrhexis nodosa (weathering due to physical damage) exhibited

similar observations. This demonstrated that there is no abnormality in the cystine-rich

proteins compared to other groups.

Therefore, the excessive structural damage observed in African-type hair is consistent

with physical trauma (e.g. grooming) rather than an inherent weakening due to any

structural abnormality.32

Thus, given the evidence and research on African hair fibres,

it is important to note that it is very unusual to find/collect a African fibre in an

untreated or virgin state.

5.2.2 FTIR-ATR Spectroscopic-Chemometric Analysis of African-type Hair Fibres

For the analysis of African-type hair fibres, 215 spectra (2-3 fibres per person and 3-5

spectra from each (dependent upon length of the fibre)) were acquired from 23

individuals of African and PNG origin. The historical record pertaining to these

individuals is presented in Appendix I. The spectra were pre-processed (Section 2.6)

and submitted to Sirius and Decision Lab for chemometric analysis.

206

5.2.2.1 Comparison of the 1750-800 cm-1

and 1690-1500 cm-1

regions

In the previous chapter (Section 4.2.1.1), African-type hair fibres were processed along

with Asian and Caucasian hair fibres. The PCA scores plot (Figure 4.2) appeared

complex due to the intense clustering on the PC2 axis and the high number of „fuzzy‟

samples present. These African-type spectra were not processed further (as per

Section 4.2.1.1) at that stage; the optimisation of the protocol was based on the

separations of Caucasian and Asian spectra only. Although it was determined that the

1690-1500 cm-1

was the most suitable range (Chapter 4, Section 4.3) to discriminate

single hair fibres, it was imperative to demonstrate that the results were similar for the

African-type hair. Therefore, the currently accepted spectral region (1750-800 cm-1

)

was compared to the proposed alternative region (1690-1500 cm-1

). The 215 available

spectra were processed by FC using a hard weighting exponent (p=1.2) and based on a

3-cluster model (i.e. untreated, mildly treated and chemically treated, 4PCs 98 %

variance) for the 1750-800 cm-1

and 1690-1500 cm-1

spectral regions. The FC

membership values for both spectral regions are presented in Appendices VI and VII

respectively.

It can be established from both the FC tables that untreated fibres (blue values),

demonstrate strong membership to column or cluster 2, chemically treated fibres (pink)

display membership of cluster 1, and mildly treated fibres (green) belong to cluster 3.

In relation to „fuzzy‟ samples (white), 104 (48 % of the total) spectra had fuzzy

membership in the 1750-800 cm-1

database and 91 spectra in the 1690-1500 cm-1

(42 %)

one. Hence, the FC analysis of the African-type hair fibres is similar to the FC analysis

of the Caucasian and Asian fibres (Section 4.2.1.4 and Table 4.13 (Section 4.3)), which

demonstrated that more non-fuzzy samples are available with the use of the 1690-1500

cm-1

region. The fuzzy samples were removed from the data matrix (215 spectra).

This matrix, free of fuzzy objects, was submitted to PCA and the scores-scores plots of

the African-type fibre database using the 1750-800 cm-1

and 1690-1500 cm-1

spectral

regions are presented in Figures 5.1 and 5.2 respectively.

207

Figure 5.1 – PC1 vs. PC2 scores plot of untreated♦, mildly treated▲ and chemically

treated fibres■ for the African-type hair fibres between 1750 - 800 cm-1

.

Figure 5.2 – PC1 vs. PC2 scores plot of untreated♦, mildly treated▲ and chemically

treated fibres■ for the African-type hair fibres between 1690-1500 cm-1

.

208

In Figure 5.1 (1750-800 cm-1

), 87.5 % of the total spectral data variance is explained by

the first two PCs. Untreated fibres (blue) have negative scores on PC1 and are

separated along the PC1 axis from chemically treated fibres (pink) which have positive

scores on PC1. However, the mildly treated fibres (green) have scores that are centred

about the origin of the PC1 and PC2 axis, in between the untreated and chemically

treated groups. As the scores of the mildly treated group are not separated by a PC, this

makes it difficult to decipher the boundaries between the three classes of fibre. In

Figure 4.5 of the Caucasian-Asian 1750-500 cm-1

database, the mildly treated group

was separated along the PC2 axis from the untreated and chemically treated fibres. This

result demonstrates that African-type hair fibres fit the proposed protocol on the basis of

untreated or treated object separation (Figure 4.1), and also illustrates that the results of

the previous study23

(Section 1.6.4.1) were inconclusive because the total number of

samples was too small.

In Figure 5.2, 85.9 % of the total spectral data variance is explained by the first two

PCs, comparable to the total data variance explained in Figure 5.1. However, the

discrimination of the three classes is different. Untreated fibres (blue) have moderate to

high positive scores on PC1 adjacent to the mildly treated fibres which have low to

moderate positive scores on PC1. These two groups are separated along the PC1 axis

from the chemically treated fibres (pink) which have scores on negative PC1.

In Figure 5.1 the untreated scores are spread across PC2 from +10 to -10, whereas in

Figure 5.2 they are spread from +1 to -2. It is also the case that due to the FC analysis,

more fibres are classified as untreated in the 1750-800 cm-1

(33 spectra) spectral region

compared to 10 spectra using the 1690-1500 cm-1

region. Therefore, when the spectral

analysis region is shortened, i.e. the cystine oxidation region is removed, approximately

2/3 of the untreated fibres in the 1750-800 cm-1

region are then classified as mildly

treated in the 1690-1500 cm-1

region. It would appear that the bands related to the

above oxidation region are so strong that the relationship between an untreated fibre and

a chemically treated fibre is exaggerated.

The apparent classification of more untreated hair objects as mildly treated, when

studied within 1690-1500 cm-1

, suggests that the presence of different [SOn] modes of

vibration included in the 1750-1500 cm-1

region, are so varied from sample to sample

209

that they override the spectral effects of the smaller of the conformational changes of

the α-helix, random coil and β-sheet vibrations detected in the former region.

In the 1750-800 cm-1

region, the untreated and chemically treated fibres form a loose

cluster whereas in the 1690-1500 cm-1

the scores form tight clusters. For PCA models,

tight clustering of similar scores is important because this reduces the boundary of the

group on the plot, and in turn, reduces the overlap of the scores with other groups,

which can ultimately reduce the risk of misclassification of an unknown object. The

more scattered cluster noted above also suggests that the variability in the nature and

composition of the cystine oxidation products is quite significant and reduces the

possibility of spectral discrimination.

To test the hypothesis that the African-type mildly treated fibre class can be subdivided

into mild physical and mild chemical treatments (PCA Figure 4.6), an FC, 4-cluster

model of the 1750-800 cm-1

and 1690-1500 cm-1

African-type databases were calculated

(p=1.2 weight exponent). The resultant PCA scores plots (Figure 5.3 and Figure 5.4),

demonstrate that four clusters spread across the PC1 axis.

Figure 5.3 - PC1 vs. PC2 scores plot of the African-type 1750-800 cm-1

spectral

database based on a 4-cluster FC model illustrating the untreated♦, mild physical

treatment▲, mild chemical treatment■ and chemically treated■ spectral objects.

-15

-10

-5

0

5

10

15

20

-40 -30 -20 -10 0 10 20 30 40

PC

2 (

9.1

%)

PC1 (78.4 %)

Untreated Treated

Mild Physical Treatment Mild Chemical Treatment

Untreated


Mild Chemical

Treatment Chemically Treated

Increase in Chemical Treatment

210

Figure 5.4 - PC1 vs. PC2 scores plot of the African-type 1690-1500 cm-1

spectral

database based on a 4-cluster FC model illustrating the untreated■, mild physical

treatment▲, mild chemical treatment and chemically treated♦ spectral objects.

The first cluster of spectral objects (blue) is untreated hair from the African-type male

No. 1 (Appendix I), with low negative scores on PC1 and PC2 (Figure 5.3) and positive

scores on PC1 and PC2 (Figure 5.4). No fibres from the other 22 African and PNG

donors were classified as untreated according to this model. The next cluster with

moderate scores on negative PC1 and positive PC2 (Figure 5.3) and positive PC1 and

PC2 (Figure 5.4) is attributed to fibres that have experienced mild physical treatment

(turquoise). The cluster situated at the centre of the PC1 and PC2 axis (green) relates to

fibres that have been mildly treated (chemically). Finally, the cluster with high scores

on positive PC1 (Figure 5.3) and low scores on negative PC1 (Figure 5.4) is of the

African-type fibres that have been chemically treated (pink). Hence, in comparison to

Figure 4.6, a hair fibre of any race, at any given time, could be potentially in four

different chemical states as it progresses from the untreated to mildly physical, mildly

chemical and chemically treated states.

-8

-6

-4

-2

0

2

4

6

8

10

-15 -10 -5 0 5 10 15 20

PC

2 (

9.1

%)

PC1 (78.4 %)

Treated Untreated

Mild Physical Treatment Mild Chemical Treatment

Increase in Chemical Treatment

TreatedUntreated

Mild

Physical

Mild Chemical

.

211

The PC1 loading variables discriminate the untreated and mildly treated fibres from the

chemically treated fibres, Figure 5.5 (1750-800 cm-1

) and Figure 5.6 (1690-1500 cm-1

).

The PC1 loadings plot for the 1750-800 cm-1

region is analogous to the plot observed

for the Caucasian and Asian hair fibres (Figure 4.7). The plot illustrates that the

chemically treated and approximately half of the mildly treated spectral group (positive

loadings) are influenced by the frequencies between 1200-1000 cm-1

(purple) relating to

the products of the oxidation of cystine (cysteic acid at 1172 cm-1

(anti-symmetric

stretch) and 1040 cm-1

(symmetric stretch), cystine dioxide 1121 cm-1

and cystine

monoxide at 1071 cm-1

(symmetric stretch)).

Figure 5.5 - PC1 Loadings plot of the chemically treated and mildly treated African-

type spectral objects (positive loadings), and the untreated and mildly treated African-

type spectral objects (negative loadings) between 1750-800 cm-1

IR region.

212

Figure 5.6 - PC1 Loadings plot of the untreated and mildly treated African-type

spectral objects(positive loadings) and the chemically treated African-type spectral

objects (negative loadings) between 1690-1500 cm-1

IR region.

Chemically treated fibres also show higher loadings between 1750-1700 cm-1

(dark

blue), attributed to the υ (C=O) stretch of the COOH group, and to a lesser extent, weak

loadings between 1350-1265 cm-1

(dark green) assigned to the overlap of bands from

δ(CH2) deformation bending mode from the amino acid, tryptophan, at 1342 cm-1

, the

υs(SO2) stretch at 1315 cm-1

, and finally the vibrational stretches at 1284 cm-1

and 1257

cm-1

which pertain to the υ (C-N) stretch and δ (N-H) of the α-helix and random coil

(Amide III).

Conversely, the untreated fibres and the other half of the mildly treated spectral group

are related to the frequencies between 1700-1350 cm-1

and 1260-1220 cm-1

which are

attributed to the Amide I, Amide II and Amide III bands (black) at approximately 1627

cm-1

and 1515 cm-1

respectively, deformation and bending modes of the δ(C-H), (CH2)

and (CH3) groups (blue) at approximately 1461 cm-1

, 1445 cm-1

and 1392 cm-1

respectively; and lastly the Amide III band (black) of the β-sheet at approximately 1238

cm-1

.

213

The PC1 loadings plot for the 1690-1500 cm-1

region (Figure 5.6) is also similar to the

loadings analysis of the Caucasian and Asian hair fibres (Figure 4.16). The untreated

and mildly treated fibres (positive loadings) are influenced by the α-helical and β-

pleated sheet of the Amide I and Amide II bands (black) between 1660-1600 cm-1

and

1550-1500 cm-1

respectively. Conversely, the treated fibres are influenced by the

changes occurring to the Amide I υ(CONH2) stretch of the asparagine and glutamine

side chains and υ(C=O) stretch of the β-pleated sheet and random coil conformation

between approximately 1690-1670 cm-1

(dark blue); the anti-symmetric υa(C=O)

carbonyl stretch of aspartic and glutamic acid between 1590-1570 cm-1

(green); and the

vibration of the tri-substituted indole ring of tryptophan between 1570-1550 cm-1

(blue).

Hence, the pattern of loadings bands of African-type untreated, mildly treated and

chemically treated hair spectral objects are similar to those from the Caucasian and

Asian hair as per the proposed forensic protocol (1750-800 cm-1

) and the alternate

region (1690-1500 cm-1

). The current and prospective regions were further compared

using MCDM analysis.

5.2.2.2 MCDM Analysis of African-type Hair Fibres

The 111 x 2 (1750-800 cm-1

) and 124 x 2 (1690-1500 cm-1

) matrices i.e. both without

the fuzzy samples, were submitted for PROMETHEE ranking and GAIA analyses.

Tables 5.1 and 5.2 show the modelling involved for analyses of the matrices.

Table 5.1 PROMETHEE Model for African-type Untreated, Mildly Treated and

Chemically Treated Hair Spectra (1750-800 cm-1

)

Criterion PC1 PC2


Minimised/Maximised Minimised Maximised

p - -

q - -

σ 14.8184 4.7933

Unit (a.u.) (a.u.)

Weight 1.00 1.00

214

Table 5.2 PROMETHEE Model for ranking of African-type Untreated, Mildly

Treated and Chemically Treated Hair Spectra (1690-1500 cm-1

)

Criterion PC1 PC2


Minimised/Maximised Maximised Minimised

p - -

q - -

σ 6.0607 2.1530

Unit (a.u.) (a.u.)

Weight 1.00 1.00

As per Sections 4.2.1.2, the African-type spectra were analysed using a Gaussian

preference function and Minimised/Maximised settings were selected such that spectral

objects from untreated samples were preferred on each PC criterion.

Tables 5.3 and 5.4 illustrate the PROMETHEE II net ranking charts for the African-type

„fuzzy‟ free objects of the 1750-800 cm-1

and 1690-1500 cm-1

database.

For the 1750-800 cm-1

database (Table 5.3), the ranking showed that the untreated

samples (blue) are the most preferred objects in the first 28 ranks (φ = +0.981 to

+0.199). The chemically treated fibres (pink) clearly dominate the lower ranks between

φ = -0.513 to -0.825. It seems that a few treated objects mix in with the untreated ones

and vice versa, and the mildly treated objects (green) ranks 25 to 101 (φ = +0.242 to (-

0.509)) mix into the two groups with some tending to favour the treated end.

215


1 Un 0.981

2 Un 0.85

3 Un 0.842

4 Un 0.827

5 Un 0.804

6 Un 0.739

7 Un 0.708

8 Un 0.684

9 Tr 0.683

10 Un 0.645

11 Un 0.562

12 Un 0.476

13 Un 0.45

14 Un 0.447

15 Un 0.409

16 Tr 0.38

17 Un 0.373

18 Tr 0.335

19 Un 0.296

20 Un 0.278

21 Un 0.277

22 Un 0.269

23 Tr 0.268

24 Un 0.254

25 Tr 0.242

26 Tr 0.225

27 Un 0.217

28 Un 0.199

29 Tr 0.176

30 Tr 0.166

31 MT 0.164

32 Un 0.16

33 Tr 0.159

34 Tr 0.155

35 Tr 0.137

36 MT 0.131

37 Tr 0.126

38 Tr 0.122

39 MT 0.095

40 Un 0.077

41 Tr 0.067

42 Un 0.066

43 MT 0.058

44 MT 0.044

45 MT 0.043

46 Tr 0.022

47 Un 0.02

48 MT 0.015

49 MT 0.014

50 Un 0.01

51 Un 0.002

52 Un -0.003

53 Tr -0.007

54 MT -0.007

55 Tr -0.031


56 MT -0.045

57 MT -0.047

58 MT -0.049

59 Tr -0.052

60 MT -0.07

61 Tr -0.071

62 Tr -0.071

63 Tr -0.073

64 Un -0.073

65 MT -0.081

66 Tr -0.092

67 Tr -0.094

68 MT -0.101

69 Tr -0.106

70 Tr -0.106

71 Un -0.11

72 Un -0.115

73 Tr -0.128

74 MT -0.14

75 MT -0.152

76 Tr -0.154

77 MT -0.154

78 Tr -0.157

79 MT -0.168

80 MT -0.173

81 MT -0.181

82 Tr -0.19

83 MT -0.19

84 Tr -0.196

85 Tr -0.204

86 Tr -0.206

87 MT -0.213

88 Tr -0.24

89 Tr -0.269

90 MT -0.276

91 MT -0.287

92 Un -0.293

93 MT -0.331

94 MT -0.403

95 Tr -0.407

96 MT -0.408

97 Tr -0.463

98 MT -0.476

99 Tr -0.477

100 MT -0.493

101 MT -0.509

102 Tr -0.513

103 Tr -0.546

104 Tr -0.554

105 Tr -0.582

106 Tr -0.6

107 Tr -0.649

108 Tr -0.656

109 Tr -0.67

110 Tr -0.814

111 Tr -0.825

Legend



Treated (Tr) = Pink

Table 5.3 – PROMETHEE II Net φ Ranking of the African-type 1750-800 cm-1

Spectral

Database

216


1 Tr 0.951

2 MT 0.804

3 MT 0.748

4 Un 0.674

5 Un 0.666

6 MT 0.658

7 MT 0.650

8 Un 0.650

9 MT 0.626

10 Un 0.561

11 MT 0.548

12 MT 0.516

13 Tr 0.504

14 MT 0.504

15 Un 0.479

16 Un 0.475

17 MT 0.463

18 Un 0.430

19 MT 0.430

20 MT 0.422

21 MT 0.422

22 MT 0.422

23 Un 0.398

24 MT 0.365

25 MT 0.357

26 MT 0.341

27 MT 0.317

28 MT 0.317

29 MT 0.300

30 MT 0.300

31 MT 0.300

32 MT 0.284

33 Un 0.268

34 Tr 0.260

35 Tr 0.260

36 Un 0.252

37 Tr 0.243

38 MT 0.211

39 MT 0.187

40 MT 0.178

41 MT 0.178

42 MT 0.170

43 Tr 0.162

44 MT 0.154

45 Tr 0.146

46 MT 0.122

47 Tr 0.122

48 MT 0.089

49 MT 0.081

50 Tr 0.081

51 MT 0.065

52 Tr 0.065

53 Tr 0.048

54 Tr 0.048

55 MT 0.024


56 Tr 0.008

57 MT -0.008

58 Tr -0.008

59 MT -0.016

60 Tr -0.016

61 MT -0.024

62 MT -0.024

63 MT -0.048

64 MT -0.065

65 MT -0.073

66 Tr -0.097

67 MT -0.105

68 Tr -0.105

69 MT -0.130

70 MT -0.134

71 Tr -0.138

72 MT -0.138

73 MT -0.138

74 MT -0.138

75 Tr -0.146

76 MT -0.154

77 Tr -0.154

78 MT -0.170

79 Tr -0.170

80 MT -0.178

81 Tr -0.187

82 Tr -0.187

83 Tr -0.187

84 MT -0.195

85 MT -0.211

86 MT -0.219

87 Tr -0.243

88 MT -0.243

89 Tr -0.252

90 Tr -0.260

91 MT -0.268

92 MT -0.268

93 Tr -0.272

94 MT -0.284

95 Tr -0.292

96 MT -0.325

97 Tr -0.325

98 MT -0.325

99 MT -0.341

100 Tr -0.345

101 MT -0.349

102 Tr -0.357

103 Tr -0.357

104 MT -0.365

105 Tr -0.374

106 MT -0.382

107 Tr -0.382

108 MT -0.382

109 Tr -0.398

110 Tr -0.406


111 MT -0.414

112 Tr -0.430

113 Tr -0.463

114 MT -0.495

115 Tr -0.536

116 MT -0.544

117 Tr -0.552

118 Tr -0.577

119 Tr -0.577

120 Tr -0.593

121 Tr -0.601

122 Tr -0.626

123 Tr -0.666

124 Tr -0.869

Legend



Treated (Tr) = Pink

Table 5.4 – PROMETHEE II Net φ Ranking of the African-type 1690-1500 cm-1

Spectral

Database

217

In Table 5.4 (1690-1500 cm-1

), the mildly treated fibres dominate approximately the

first 42 ranks between φ = +0.804 – (+0.170) in which the small number of the

untreated samples is scattered. The chemically treated fibres dominate the middle to

lower ranks (objects 87 to 124) between φ = -0.243 to (-0.869). Again, the objects in

the middle (φ = 0.163 to (-0.22)) scatter indicating the similarity of the hair classes.

Tables 5.3 and 5.4 emphasise that the majority of African-type fibres are likely to be

physically and/or chemically treated, partly because of the shape and curvature of the

hair. Normal grooming habits with African-type hair place extra stress on the fibres as

compared to the grooming of Asian and Caucasian hair which is less curly.281

The GAIA biplots for the African-type 1750-800 cm-1

and 1690-1500 cm-1

database are

presented in Figures 5.7 and 5.8 respectively. In total, 100 % of the data variance is

accounted for by the two GAIA PCs, hence, all the information has been retained on the

GAIA planes. These biplots show that the spectral objects are separated into three

somewhat overlapping groups analogous to Figure 5.1 and 5.2 - the untreated (blue)

mildly treated (green) and treated (pink) fibres.

In the 1750-800 cm-1

region (Figure 5.7), the two criteria vectors, PC1 and PC2 (black),

are orthogonal to each other where PC1 favours the untreated objects with positive

scores while the chemically treated objects mostly have negative scores. Similarly PC2

separates mostly chemically treated objects (positive scores) from untreated ones

(negative scores). Thus, the two groups are separated on that basis. The Π decision

axis (red vector) is very strong, indicating a robust decision, pointing towards the

untreated fibre class. In Figure 5.8 the PC1 criterion vector is related to the untreated

spectral objects whilst the PC2 criterion vector favours the mildly treated objects. The

decision axis points along the PC2 axis. Hence, the plot demonstrates a similar

distribution of the 176 spectra as in the PCA scores-scores plot (Figure 4.5, Section

4.2.1.1) of the three classes providing supporting evidence that three classes of fibre

exist.

218

Δ 100 %

Figure 5.7 - GAIA analysis of the 111 spectra for the African-type hair fibre database


; ▲untreated fibres, ■ chemically treated fibres, ■ mildly

treated hair fibres, ● pi (Π) decision-making axis, and ■ PC1 and PC2 criteria using a


Untreated

Mild Treatment

Chemically Treated

PC2

PC1

219

Δ 100 %

Figure 5.8 - GAIA analysis of the 124 spectra for the African-type hair fibre database


; ■untreated fibres, ■ chemically treated fibres, ■ mildly

treated hair fibres, ● pi (Π) decision-making axis, and ■ PC1 and PC2 criteria using a


Chemically Treated

Mild Treatment Untreated

PC2

PC1

220

Thus, the similar discrimination between the untreated, mildly treated and chemically

treated spectral objects from the African-type hairs compared well with Asian and

Caucasian objects, and now suggests that the spectral objects from the three races may

be compared.

5.3.1 Incorporation of the African-type Hair IR Spectra to the Protocol

According to Panayiotou‟s forensic protocol (Figure 4.1, Section 4.1), the systematic

order of separation of the spectral objects is based sequentially on treatment, gender and

race. Consequently, in this section this protocol was applied for the first time to a

matrix which included African and PNG IR spectral objects.

5.3.1.1 Chemometric Analysis of the Entire (3 Races) Database

The inclusion of the African-type spectral database with the Asian and Caucasian

spectral database was approached with caution to avoid any misrepresentations of the

data. As observed in Figure 4.2, the inclusion of the African-type spectra produced

severe overlapping of the objects precluding any useful analysis. Therefore, to reduce

the complexity of the analysis, only the untreated and treated Asian and Caucasian

spectral objects as well as with the untreated and treated African-type spectra were first

processed by PCA using the alternative region, 1690-1500 cm-1

(Figure 5.9). In this

matrix, in addition to the samples of the three races, the two typical reference groups,

CFTR10 and CFUN1 (Chapters 3.0 and 4.0), were included for comparison. With

respect to these spectral objects, CFUN1 and CFTR10, it can be seen that the untreated

spectral objects (blue) have positive scores on PC1 and PC2, and are separated by the

PC1 axis from the treated spectral objects (pink) with negative scores on PC1. Situated

amongst the loose cluster of untreated objects are the African-type untreated spectral

objects (black), and the treated African-type objects (purple) form a tight cluster with

the treated objects. At the cross-section of the PC1 and PC2 axes, there is a clear

separation of the untreated and treated spectral objects.

221

Figure 5.9 – PCA scores plot of the 1690 -1500 cm-1

IR Database; Caucasian and Asian

untreated fibres●, chemically treated fibres■, with the inclusion of the untreated

African-type untreated♦ and chemically treated■ African-type spectral objects.

When the mildly treated Asian and Caucasian (green) and the mildly treated African-

type objects (brown) were added to the data matrix, the resulting PCA plot is shown in

Figure 5.10. In total 88.9 % of the total data variance is explained by the first two PCs

with 72.3 % on PC1 and 16.6 % on PC2. The mildly treated Caucasian, Asian and

African-type spectral objects form a fairly tight cluster mostly with negative scores on

PC2 and between the chemically treated and untreated clusters. Thus, the African-type

spectral objects in the 1690-1500 cm-1

region, are found together with the respective

spectral objects of the Caucasian and Asian objects, i.e. the African-type hairs behave

similarly to the Asian and Caucasian ones.

222

Figure 5.10 - PCA scores plot of PC1 vs. PC2 of the Entire 1IR Database between

1690 -1500 cm-1

. Caucasian and Asian untreated fibres●, chemically treated fibres■,

mildly treated fibres▲ and African-type untreated♦, mildly treated▲ and chemically

treated■ hair fibres.

A PROMETHEE II model (Table 5.5) was created to rank order the spectral objects of

the entire (3 race) database for the 1690-1500 cm-1

spectral analysis region.

Table 5.5 PROMETHEE II Model of the Entire Spectral Database (257 spectra x

3PC Criteria) within the 1690-1500 cm-1

Spectral Region



Minimised/Maximised Maximised Maximised Maximised

p - -

q - -

σ 6.19 2.76 1.82


Weight 1.00 1.00 1.00

-10

-5

0

5

10

15

20

-20 -15 -10 -5 0 5 10 15 20

PC1 (72.3 %)

PC

2 (

16

.6%

)

Untreated Treated Mildly Treated

Untreated Negroid Chemically Treated Negroid Mildly Treated Negroid

Increase in

Physical/Chemical Treatment

CFUN 1

CFTR 10

Untreated

Mildly Treated

Chemically Treated

Untreated

Untreated African-type

Treated

Treated African-type

Mildly Treated

Mildly Treated African-type

223

The Net φ ranking (Table 5.6) for the entire spectral database was between

+0.833>φ>0-0.153 with the untreated spectral reference, CFUN 1 – CFUN 110 samples

(blue) as the most preferred objects between +0.833 to (+0.544). This was followed by

the African-type untreated (NUN) spectral objects (grey) between approximately

+0.484 to (+0.395).

The remainder of the spectral objects between ranks 43 and 194 is very scattered. The

middle ranking from 80 to 166 is dominated by the Asian and Caucasian chemically

treated samples (TR, pink) and the African-type treated objects (NTR, purple) between

φ = +0.162 to (-0.168). The mildly treated objects (MTR, green) dominate the lower

rankings (195-257) from φ = -0.267 to (-0.557). Therefore, the ordering starts from the

untreated spectra to the chemically treated spectra and finishes with the mildly treated

spectra.

The GAIA bi-plot for the 1690-1500 cm-1

spectral analysis region is presented in Figure

5.11. The untreated Asian-Caucasian (blue ■) and untreated African-type (black ■)

objects have positive scores on PC1 and negative scores on PC2, and are separated

along the PC1 axis from the treated Asian-Caucasian (pink ■) and African-type (purple

■) which have negative scores on PC1 and PC2. The mildly treated Asian-Caucasian

(light green ■) and African-type (brown ■) have positive scores on PC2 and spread

across the PC1 axis. The PC3 (turquoise ■) vector favours the untreated samples whilst

the PC1 and PC2 vectors point towards the periphery of the mildly treated and treated

samples respectively. The decision axis (red ●) favours the untreated spectral objects

which contain the reference CFUN1-CFUN110 samples.

224


1 CFUN18 0.833

2 CFUN17 0.816

3 CFUN13 0.767

4 CFUN16 0.705

5 CFUN14 0.703

6 CFUN110 0.674

7 NUN 0.665

8 NUN 0.654

9 CFUN11 0.648

10 NUN 0.647

11 CFUN15 0.644

12 CFUN19 0.628

13 UN 0.586

14 UN 0.581

15 UN 0.547

16 CFUN12 0.544

17 NUN 0.538

18 UN 0.523

19 NUN 0.508

20 UN 0.501

21 NMT 0.499

22 NMT 0.485

23 NUN 0.484

24 NUN 0.474

25 NUN 0.474

26 UN 0.453

27 NUN 0.451

28 UN 0.444

29 NUN 0.442

30 NUN 0.438

31 NUN 0.436

32 NMT 0.434

33 NMT 0.416

34 NUN 0.405

35 NUN 0.395

36 UN 0.39

37 UN 0.382

38 UN 0.377

39 TR 0.374

40 UN 0.367

41 UN 0.366

42 UN 0.36

43 MTR 0.359

44 MTR 0.359

45 MTR 0.354

46 NUN 0.346

47 NTR 0.344

48 TR 0.334

49 NUN 0.33

50 MTR 0.324

51 NUN 0.322

52 TR 0.314

53 TR 0.305

54 NMT 0.303


56 NUN 0.288

57 TR 0.271

58 NUN 0.269

59 NMT 0.267

60 NMT 0.26

61 NTR 0.257

62 MTR 0.257

63 NUN 0.256

64 UN 0.248

65 MTR 0.246

66 NMT 0.243

67 MTR 0.231

68 NUN 0.218

69 TR 0.212

70 MTR 0.204

71 MTR 0.192

72 NTR 0.189

73 NMT 0.185

74 NMT 0.184

75 MTR 0.175

76 MTR 0.172

77 MTR 0.17

78 MTR 0.169

79 NUN 0.164

80 TR 0.162

81 NMT 0.159

82 NUN 0.136

83 TR 0.132

84 NMT 0.126

85 TR 0.111

86 TR 0.105

87 NMT 0.102

88 MTR 0.099

89 MTR 0.095

90 TR 0.089

91 TR 0.08

92 NTR 0.086

93 MTR 0.081

94 TR 0.08

95 MTR 0.08

96 NTR 0.078

97 MTR 0.076

98 MTR 0.07

99 CFTR105 0.07

100 NTR 0.066

101 TR 0.066

102 CFTR103 0.065

103 NTR 0.062

104 MTR 0.05

105 MTR 0.047

106 CFTR101 0.0447

107 CFTR102 0.0436

108 MTR 0.0415

109 TR 0.0414


110 NTR 0.0352

111 MTR 0.0217

112 TR 0.0153

113 MTR 0.0134

114 NMT 0.0107

115 MTR 0

116 NMT -0.017

117 MTR -0.021

118 TR -0.033

119 NMT -0.035

120 CFTR106 -0.037

121 MTR -0.038

122 CFTR104 -0.039

123 NTR -0.043

124 MTR -0.043

125 MTR -0.043

126 TR -0.044

127 MTR -0.052

128 TR -0.054

129 MTR -0.057

130 TR -0.064

131 NTR -0.069

132 TR -0.069

133 NTR -0.07

134 NMT -0.077

135 TR -0.082

136 NMT -0.089

137 TR -0.091

138 MTR -0.104

139 TR -0.106

140 NMT -0.106

141 MTR -0.106

142 NTR -0.108

143 MTR -0.114

144 NTR -0.122

145 NTR -0.122

146 MTR -0.125

147 NTR -0.128

148 MTR -0.1299

149 NTR -0.131

150 TR -0.131

151 NTR -0.131

152 TR -0.132

153 MTR -0.132

154 MTR -0.134

155 TR -0.1358

156 MTR -0.1359

157 MTR -0.1391

158 TR -0.141

159 MTR -0.143

160 TR -0.145

161 TR -0.147

162 CFTR1011 -0.153

163 NMT -0.153

Table 5.6 - PROMETHEE II Net φ Ranking of the 3 Race IR Spectral Database 1690-1500 cm-1

225


164 NTR -0.162

165 TR -0.164

166 TR -0.168

167 MTR -0.172

168 MTR -0.173

169 MTR -0.176

170 MTR -0.183

171 TR -0.187

172 MTR -0.19

173 TR -0.192

174 NTR -0.196

175 TR -0.197

176 MTR -0.198

177 MTR -0.2

178 NMT -0.208

179 MTR -0.214

180 NTR -0.216

181 NTR -0.217

182 MTR -0.219

183 TR -0.225

184 NTR -0.226

185 TR -0.231

186 MTR -0.237

187 TR -0.238

188 NTR -0.241

189 TR -0.251

190 MTR -0.254

191 CFTR108 -0.256

192 NTR -0.257

193 NTR -0.257

194 TR -0.263

195 MTR -0.267

196 MTR -0.27

197 TR -0.271

198 MTR -0.273

199 MTR -0.273

200 MTR -0.273

201 TR -0.275

202 MTR -0.278

203 MTR -0.279

204 MTR -0.279

205 MTR -0.28

206 MTR -0.28

207 TR -0.283

208 TR -0.288

209 MTR -0.29

210 NTR -0.292

211 TR -0.299

212 TR -0.301

213 MTR -0.303

214 TR -0.303

215 MTR -0.314

216 MTR -0.317

217 MTR -0.326


218 TR -0.329

219 MTR -0.335

220 MTR -0.342

221 MTR -0.342

222 MTR -0.343

223 MTR -0.343

224 MTR -0.344

225 MTR -0.345

226 NTR -0.351

227 TR -0.353

228 MTR -0.364

229 MTR -0.364

230 CFTR109 -0.37

231 TR -0.37

232 MTR -0.371

233 MTR -0.374

234 MTR -0.383

235 NTR -0.387

236 CFTR1010 -0.398

237 MTR -0.399

238 MTR -0.409

239 NTR -0.411

240 MTR -0.415

241 MTR -0.417

242 MTR -0.419

243 MTR -0.43

244 MTR -0.434

245 MTR -0.434

246 MTR -0.436

247 TR -0.44

248 MTR -0.444

249 MTR -0.46

250 MTR -0.46

251 MTR -0.463

252 MTR -0.475

253 MTR -0.477

254 MTR -0.493

255 MTR -0.513

256 MTR -0.514

257 MTR -0.557

Legend

Untreated (UN) = Blue

African-type Untreated (NUN) = Black

Mildly Treated (MTR) = Green

African-type Mildly Treated = Brown

Treated (TR) = Pink

African-type Treated (NTR) = Purple


226

Δ 74.5 %

Figure 5.11 – GAIA analysis of the 257 spectra for the Entire (3 Race) IR database


; ■untreated fibres, ■ untreated African-type fibres, ■

chemically treated fibres, ■ chemically treated African-type fibres, ■ mildly treated hair

fibres, ■mildly treated African-type fibres, ● pi (Π) decision-making axis, and ■

Original PC1, PC2 and PC3 criteria using a Gaussian preference function.

PC2

PC1

UNTREATED

TREATED

MILDLY TREATED

227

The results are different when the spectral objects of the entire database were examined

over the 1750-800 cm-1

region. For the current 1750-800 cm-1

region (Figure 5.12),

92.8 % of the total data variance is explained by the first two PCs with 78.4 % on PC1

and 14.4 % on PC2. The PC plot is complex and does not offer a clear separation of the

fibre classes. However, the plot provides a trend rather than groupings, and is most

useful as a 2-D pattern. If all the objects are projected onto PC2, then there is very little

definitive separation observed. However, if the distribution of objects is viewed in the

two dimensional PC space, a trend pattern emerges which suggests that all treated

samples are grouped together with positive scores on PC1, while the untreated samples,

group on PC1 with negative scores. The mildly treated groups are evident between the

previous two, and arguably, most mildly treated African-type samples (▲) are separated

on PC2 with positive scores from most of the mildly treated objects (▲) with negative

scores. Thus, the overall pattern of objects suggests a trend which indicates grouping

according to treated, mildly treated and untreated classes on PC1. In addition, while the

treated groups remain unseparated, the mildly treated ones indicate some separation and

the untreated ones form loose unique groups.

Figure 5.12 - PCA scores plot of PC1 vs. PC2 of the 1750-800 cm

-1IR Database.

Caucasian and Asian untreated fibres●, chemically treated fibre■, mildly treated

fibres▲, and African-type untreated♦, mildly treated▲ and chemically treated■

spectral objects.

-30

-25

-20

-15

-10

-5

0

5

10

15

20

-50 -40 -30 -20 -10 0 10 20 30 40

PC

2 (

14

.4%

)

PC1 (74.8%)

Untreated Treated Mild Treatment

Untreated African-type Treated African-type Mild Treatment African-type

Untreated

Mildly Treated

Chemically Treated

CFUN1 CFTR10

228

It is reasonable to suggest that each of the African-type hair classes (i.e. untreated,

mildly treated and chemically treated) is not associated with their respective Caucasian

and Asian hair classes using the 1750-800 cm-1

spectral region. The structural

chemistry at the molecular level of mildly treated and chemically treated African-type

fibres is different from similarly treated Caucasian and Asian fibres for one main

reason. A goal of cosmetic treatments for African men and women is to have

straightened/permed and coloured hair. This requires that the hair is subjected to a

number of multiple treatments to achieve the desired outcome. Hence, this would

increase the moderate levels of cysteic acid in the chemically untreated hair to quite

high levels, which as PCA in this study indicated, differentiates the treated African-type

fibres from treated Caucasian and Asian hair. The latter types of hair usually will have

had only one treatment. This supports the finding from Panayiotou22

who was able to

demonstrate the discrimination of chemically treated hair on the basis of single versus

multiple cosmetic treatments.

These results support the conclusions from the previous chapter, which suggested that

the optimum region for analysing hair keratin IR spectra was between 1690-1500 cm-1

.

Furthermore, the results also provided an explanation for why African-type spectral

objects did not fit into the protocol design from the previous investigation (Section

1.6.4.1) where it was established that the separation of the African-type spectra on the

basis of chemical treatment appeared to contradict the model. In that case, the studied

region was between 1750-800 cm-1

, which contained spectral elements i.e. products of

cystine oxidation, as described above, that precluded the separation of the various

classes. When using the 1690-1500 cm-1

region to analyse keratin FTIR-ATR spectra,

the principal differences between the spectra are fundamentally based on α-helical, β-

sheet and random coil conformations. This region of the spectrum is more suitable for

the matching and discrimination of the spectra from different fibres than the

1750-800 cm-1

range. In this region, FC and PCA misclassify an untreated African-type

fibre for a mildly or chemically treated fibre due to inconsistent amounts of cysteic acid

in the cuticle. Hence, subsequent sections focus on the analysis of keratin spectra


.

229

5.3 Gender: Male vs. Female Hair Fibres

In criminal cases, it is relevant to forensically identify the gender of the hair sample. In

one of the earliest studies, Hopkins et al.158

using peak ratio differences concluded that

no differences could be discerned between the Amide I and II bands. However, in more

recent studies, Panayiotou24

and Barton23

had proposed that the Amide I and II

vibrational bands were responsible for the discrimination of male and female, untreated

and chemically treated hair fibres. This result was demonstrated with the use of

Chemometrics, which was a more sophisticated approach. Hence, the rationale of this

section is to investigate the protocol for matching and discriminating spectral objects by

comparing untreated, mildly treated and chemically treated hair fibres from subjects of

different genders

5.3.1 Gender Differences between Untreated, Mildly Treated and Chemically

Treated Fibres

5.3.1.1 Untreated Hair Fibres

Thirty nine male and female spectra (29 female (20 Caucasian, 9 Asian) and 10 male

(African-type)) from untreated fibres (excluding any fuzzy objects) were selected from

the entire database (Section 5.2.2.1), and processed separately by FC and PCA. This

data subset included the spectral reference sets; Caucasian female No. 1(Appendix I),

which is a collection of spectra from untreated hair fibres. A 2-cluster FC analysis

(male and female groups) was performed to exclude misclassified objects. Of the entire

database of male hair fibre spectra, and 10 spectra pertaining to African-type male No. 1

(NMUN 1) were deemed as untreated by this classification method. The resultant PCA

scores plot is presented in Figure 5.13. In total, 89.9 % of the total data variance is

retained by the first two PCs with 60.3 % on PC1 and 29.6 % on PC2. The spectra of

NMUN 1 (blue) form a cluster on PC2 (positive scores), and are separated along the

PC2 axis from untreated female fibres (pink), which exhibit negative scores on PC2.

The separation of spectral objects from hairs of different gender is confirmed by the

position of the CFUN1 reference objects which have negative scores on PC2 and consist

of female untreated spectra. The separation of untreated hair fibres by gender is

consistent with previous investigations.22

23

230

Figure 5.13 - PCA scores plot of PC1 vs. PC2 of the Untreated Hair Fibre Spectral

Database illustrating the separation of untreated African-type Male No.1♦ from

untreated Female■ spectral objects along the PC2 axis.

With reference to the PC2 loadings plot (Figure 5.14), the vibrational bands significant

to each gender can be discerned. The positive loadings (black), attributed to the male

spectra are influenced by the β-sheet conformation of the Amide I and Amide II bands


and 1520-1500 cm-1

respectively. The negative PC2 loadings

correspond to the female untreated spectral objects on PC2 (negative scores). The IR

spectral region between 1590-1520 cm-1

include the υa(CO2-) (green), tryptophan (blue)

and α-helix (purple) of the Amide II band.

Comparing these results with the raw and second derivative spectra (Figure 3.8,

Section 3.2.1.2 and Figure 3.17, Section 3.3.2), it appears that the untreated male hair

fibres are discriminated by the β-pleated sheet conformation (Amide II band) in the

protein of the cuticle in the fibre. Alternatively, the untreated female fibres are

described by the α-helical conformation of the Amide II in the hair cuticle. With

correlation to the chemical composition of male and female spectra, the PC2 loadings

plot provided corroborative evidence for the difference spectra between genders within

each race (i.e. of untreated spectra (Section 3.3.2)). From that evidence, it is suggested

that female hair IR spectra exhibit more intense absorption of the amino acids

tryptophan, aspartic and glutamic acid.

231

Figure 5.14 – PC2 Loadings plot of the untreated African-type Male No. 1 spectral

objects (positive loadings) and the untreated Female spectral objects (negative

loadings).

MCDM analysis was utilised to provide further verification of the separation (i.e.

quantitatively) between NMUN 1 (10 spectra) and the untreated female spectra (29

spectra CFUN1 inclusive). The 39 spectra x 2 (PC Criteria) matrix was submitted to

PROMETHEE ranking and GAIA analysis (Model - Table 5.7).

Table 5.7 PROMETHEE II Model of Untreated African-type Male (NMUN 1) and

Untreated Female Hair Spectra

Criterion PC1 PC2



p - -

q - -

σ 5.65 3.95

Unit (a.u.) (a.u.)

Weight 1.00 1.00

232

Table 5.8 illustrates the PROMETHEE II ranking for the two selected individuals from

the untreated database. The φ values ranged from 0.731<φ<-0.770. The ranking

showed that the untreated female objects (pink) are the most preferred samples between

φ = +0.731 – (-0.023) and φ = -0.107 – (-0.242), which contain the reference untreated

CFUN 1 samples. The untreated African-type male spectral objects (NMUN 1)

dominate the lower ranks between φ = -0.30 - (-0.77). The separation of gender is

indicated by the large change in φ indices between ranks 30 and 31. The GAIA bi-plot

(Figure 5.15) shows that PC1 and PC2 criteria favour the female untreated spectral

objects (pink) as indicated by the decision axis (PC; red line). The untreated female

spectral objects are separated on PC2 from the untreated African-type male objects

(blue) which have positive scores on this PC. As with PROMETHEE ranking, there are

a few overlapping spectral objects.

233


1 FUN1 0.731

2 FUN2 0.653

3 CFUN18 0.592

4 FUN4 0.518

5 FUN5 0.507

6 FUN6 0.498

7 FUN7 0.481

8 CFUN19 0.421

9 FUN9 0.385

10 FUN10 0.360

11 CFUN110 0.334

12 FUN12 0.32

13 CFUN17 0.281

14 FUN14 0.137

15 FUN15 0.120

16 FUN16 0.112

17 FUN17 0.046

18 FUN18 0.020

19 CFUN16 0.016

20 CFUN13 -0.003

21 FUN21 -0.023

22 NMUN17 -0.060

23 CFUN11 -0.090

24 NMUN15 -0.104

25 FUN25 -0.107

26 CFUN15 -0.170

27 CFUN14 -0.183

28 FUN28 -0.222

29 CFUN12 -0.223

30 FUN30 -0.242

31 NMUN12 -0.299

32 NMUN16 -0.354

33 NMUN11 -0.500

34 NMUN13 -0.572

35 FUN35 -0.597

36 NMUN14 -0.609

37 NMUN18 -0.649

38 NMUN110 -0.756

39 NMUN19 -0.770

Table 5.8 - PROMETHEE II Net φ Ranking of the Untreated Spectral Database

Legend

Female Untreated (FUN) = Pink

African-type Male Untreated (NMUN) = Blue

234

Δ 100 %

African-type Male

Untreated No. 1

Female Untreated

PC2

PC1

Figure 5.15 - GAIA analysis of the 39 spectra for the Untreated hair

fibre database; ■ Male untreated fibres, ■ Female untreated fibres, ●

pi (Π) decision-making axis, and ■ PC1 and PC2 criteria using a


235

5.3.1.2 Mildly Treated Hair Fibres

In total, 161 spectra (Sections 4.2.2.2 and 5.2.2.2) were classified as Mildly Treated by

FC (Appendix III) within the 1690-1500 cm-1

spectral range. The data matrix consisted

of 50 female spectra (15 Asian, 20 Caucasian and 15 African-type) and 111 male

spectra (41 Asian, 18 Caucasian, and 52 African-type spectra). As African-type spectral

data were the novel subset with relation to the protocol, the mildly treated Asian and

Caucasian spectral subset were analysed by PCA initially.

The PCA scores plot of the Asian and Caucasian mildly treated database is presented in

Figure 5.16. In total, 81.0 % of the total data variance is retained by the first two PCs

with 67.0 % on PC1 and 14.0 % on PC2. No separation could be discerned along the

PC1 axis, however, the objects were discriminated along the PC2 axis, where the male

mildly treated objects (blue) formed a cluster with negative scores on PC2 and the

mildly treated female objects (pink) have positive PC2 scores. Subsequently, the male

and female African-type mildly treated spectral objects were added and calculated by

PCA (Figure 5.17). The majority of the 67 male-female African-type spectral objects

((green) with the exception of approximately 7 objects) were scattered along the PC1

axis and inter-dispersed with the mildly treated female objects with positive scores on

PC2. As the majority of the mildly treated African-type database consisted of male

spectra (78 %), the separation across the PC2 axis demonstrates that male mildly treated

African-type spectra have minute structural similarities with male mildly treated Asian

and Caucasian spectra. Hence, in terms of the outline of the protocol methodology, the

African-type female-male mildly treated objects should be processed by PCA separately

from the mildly treated Asian and Caucasian spectral objects.

236

Figure 5.16 - PCA scores plot of PC1 vs. PC2 of the Mildly Treated Hair Fibre

Spectral Database illustrating the separation of mildly treated male♦ from mildly

treated female♦ spectral objects.

Figure 5.17 - PCA scores plot of PC1 vs. PC2 of the Mildly Treated Hair Fibre

Spectral Database illustrating the separation of mildly treated male♦ from mildly

treated female♦ and mildly treated African-type▲ spectral objects.

237

The structural differences between the mildly treated male and female spectral objects

(Asian and Caucasian) are described by the PC2 loadings diagram (Figure 5.18). The

female mildly treated objects (positive loadings) are ascribed to the intensity increase of

the β-pleated sheet and concomitant shift of the Amide I and Amide II band, tryptophan,

and asymmetric carboxylate νa(CO2-) vibrational band as a result of treatment. The

negative loadings, which describe the male mildly treated spectral objects are assigned

to the β-sheet, random coil and α-helix of the Amide I vibration.

Figure 5.18 – PC2 Loadings plot of the Mildly Treated spectral database showing the

separation of mildly treated female spectral objects from mildly treated male spectral

objects on the PC2 axis illustrated in Figure 5.16.

Again, as per the untreated hair spectra scenario (Section 5.3.1.1.), the correlation

between the second derivative spectra (Figure 3.18) and PC2 loadings suggest that mild

chemical treatment has a greater effect on females than males due to the increase in

intensity of the β-sheet and random coil (Amide I and II band) protein conformations

and de-protonation of aspartic and glutamic acid in females fibres. The loadings also

support the hypothesis that female spectra exhibit strong intensity of the tryptophan

vibration at 1554 cm-1

.

238

A PROMETHEE model was constructed (Table 5.9) using 2PC criteria (81 % data

variance) to provide a quantitative analysis of the separation between Asian and

Caucasian, male-female, mildly treated objects. As the mildly treated male spectra

made up the majority of the database the PC1 and PC2 criteria were maximised and

minimised respectively so they would be the preferred objects.

Table 5.9 PROMETHEE II Model of Male and Female Mildly Treated Hair

Spectra

Criterion PC1 PC2



p - -

q - -

σ 6.25 2.65

Unit (a.u.) (a.u.)

Weight 1.00 1.00

Table 5.10 demonstrates the complete ranking of the spectra of the 94 male and female

mildly treated spectral objects. The net φ values ranged from 0.911>φ>-0.747. The

male mildly treated (MMTR) spectral objects dominate approximately the first 48 ranks

from φ = +0.911 to (-0.012) followed by the female mildly treated (FMTR) which

approximately dominate the last 44 ranks between φ = -0.027 – (-0.747). It can be seen

that there is some scatter between the male and female spectral objects. Nevertheless, it

is suggested that the genders are well separated on the extremities of the ranking.

239

Table 5.10 - PROMETHEE II Net φ Ranking of the Mildly Treated Spectral

Database

Legend

Male Mildly Treated

(MMTR) = Blue

Female Mildly Treated

(FMTR) = Pink


1 MMTR 0.911

2 MMTR 0.872

3 MMTR 0.794

4 MMTR 0.743

5 MMTR 0.557

6 MMTR 0.517

7 MMTR 0.509

8 MMTR 0.427

9 MMTR 0.400

10 MMTR 0.394

11 MMTR 0.391

12 MMTR 0.386

13 MMTR 0.385

14 MMTR 0.368

15 MMTR 0.364

16 MMTR 0.358

17 MMTR 0.344

18 FMTR 0.316

19 MMTR 0.286

20 MMTR 0.270

21 MMTR 0.259

22 MMTR 0.241

23 MMTR 0.229

24 FMTR 0.204

25 MMTR 0.203

26 MMTR 0.197

27 FMTR 0.1877

28 MMTR 0.175

29 MMTR 0.170

30 MMTR 0.161

31 MMTR 0.136

32 MMTR 0.121

33 FMTR 0.109

34 MMTR 0.108

35 MMTR 0.094

36 MMTR 0.092

37 FMTR 0.088

38 MMTR 0.076

39 MMTR 0.061

40 MMTR 0.046

41 FMTR 0.039

42 MMTR 0.023

43 FMTR 0.020

44 FMTR 0.017

45 MMTR 0.011

46 FMTR -0.009

47 MMTR -0.010

48 MMTR -0.012

Rank Object

Net φ Index

49 MMTR -0.015

50 FMTR -0.027

51 FMTR -0.031

52 FMTR -0.034

53 FMTR -0.04

54 MMTR -0.052

55 FMTR -0.061

56 MMTR -0.063

57 MMTR -0.068

58 MMTR -0.070

59 FMTR -0.070

60 MMTR -0.075

61 FMTR -0.081

62 MMTR -0.099

63 MMTR -0.111

64 FMTR -0.126

65 MMTR -0.128

66 FMTR -0.134

67 FMTR -0.157

68 MMTR -0.177

69 MMTR -0.186

70 MMTR -0.193

71 FMTR -0.195

72 MMTR -0.204

73 FMTR -0.215

74 FMTR -0.226

75 MMTR -0.248

76 MMTR -0.253

77 FMTR -0.261

78 FMTR -0.276

79 FMTR -0.294

80 MMTR -0.337

81 MMTR -0.371

82 MMTR -0.395

83 FMTR -0.408

84 FMTR -0.466

85 MMTR -0.494

86 FMTR -0.564

87 FMTR -0.602

88 MMTR -0.628

89 FMTR -0.648

90 FMTR -0.656

91 FMTR -0.703

92 FMTR -0.713

93 FMTR -0.725

94 FMTR -0.747

240

A GAIA bi-plot for the Asian and Caucasian mildly treated spectra would have been

superfluous as it is very similar to Figure 5.16. In its place, a GAIA bi-plot (Δ 70.86 %)

was processed which included the African-type male and female mildly treated spectra

(Figure 5.19) which included PC3 as a third criterion. The mildly treated male spectral

objects (blue) have negative scores on PC2 separated from the female (pink) and

African-type mildly treated (green) objects which have positive scores on PC2. The

criteria vectors for GAIA can be useful as they illustrate what samples are associated

with which variables, so when unknown samples are added the analyst has an

approximate estimation of what type of samples they are. In this scenario, the PC

scores from PCA are the criteria. The PC1 criterion is approximately associated with

the female mildly treated samples; the PC2 criterion allied with the male mildly treated

objects and the PC3 criterion correlated with the African-type male and female mildly

treated spectral objects.

241

Δ 70.86 %

Figure 5.19 - GAIA analysis of the spectra for the Mildly Treated hair fibre database;

■ Male mildly treated fibres, ■ Female mildly treated fibres, ■ African-type male-

female mildly treated fibres, ● pi (Π) decision-making axis, and ■ PC1, PC2 and PC3


Male Mildly Treated

Female and African-type (Female

and Male) Mildly Treated PC2

PC1

242

5.3.1.3 Chemically Treated Hair Fibres

The 123 male and female chemically treated spectra were separated from the main

database (Section 5.2.2.1) and initially processed by FC (2-cluster model to allow for

male and female classes, p = 1.2 (hard exponent), n = 0.5). With the two cluster model,

a total of 38 spectra were misclassified, where 25 spectra pertained to the African-type

female fibres. They potentially belong to a group referred as “multiple-treated” fibres,

which had been proposed by Panayiotou.22

Hence, a FC 4-cluster model (Appendix IX,

p=1.2, 4PCs 96.7 %) was applied in an attempt to include the African-type male and

female “multiple treated” spectral objects. The 4-cluster model indicated only 14

misclassified spectra.

The PCA plot of the remaining 109 chemically treated spectral objects is presented in

Figure 5.20. This data subset included the spectral references, treated Caucasian female

No. 10 (CFTR10, Appendix I), which is a collection of spectra from chemically treated

hair fibres. In total, 82.8 % of the total data variance is retained by the first two PCs

with 65.2 % on PC1 and 17.6 % on PC2. Most chemically treated male spectral objects

(blue) have a range of positive scores on PC1 and mostly negative scores on PC2

whereas the chemically treated female spectral objects (pink) have high scores on

positive PC1 and PC2. These objects have positive PC2 scores and compare well with

the typically treated CFTR10 spectral objects. In somewhat similar circumstances to

the previous scenario (Section 5.3.1.2.), the chemically treated male and female

African-type spectral objects cluster with the treated male objects which have moderate

positive scores on PC1 and negative ones on PC2.

243

Figure 5.20 - PCA scores plot of PC1 vs. PC2 of the Chemically Treated Hair Fibre

Spectral Database illustrating the separation of treated male■, African-type male

treated■ African-type female treated▲ from treated female♦ on the PC2 axis.

The loadings plot variables that approximately separate the genders described in Figure

5.18 are the same for the chemically treated fibres. This outcome further reinforces the

hypothesis that female spectra are characterised by the α-helix of the Amide II band and

male spectra are described by the concomitant increase in intensity of the β-pleated

sheet in both the Amide I and II bands as a consequence of treatment.

A PROMETHEE II model using the PC1, PC2 and PC3 scores (c.a. 93 % data variance)

as criteria was constructed (Table 5.11) to provide a quantitative analysis of the

separation between male and female chemically treated spectral objects. To set a

reference point, the PPROMETHEE model was setup in order for the typically treated

Caucasian female No. 10 (CFTR10) samples to be the preferred objects.

244

Table 5.11 PROMETHEE II Model of Male and Female Chemically Treated Hair

Spectra



Minimised/Maximised Minimised Minimised Minimised

p - -

q - -

σ 5.82 2.89 2.14


Weight 1.00 1.00 1.00

The PROMETHEE II ranking output (Table 5.12) for the chemically treated database

was in the φ range of +0.725>φ>-0.552, where female treated objects (FTR, pink) were

the most preferred objects (φ: +0.725 to (-0.007)), CFTR10 treated reference samples

inclusive. Scattered amongst the ranking of FTR and male treated (MTR) spectral

objects were the African-type female treated objects (NFTR, green) and φ -0.01 to (-

0.137) and φ -0.245 to (-0.322). The treated male spectral (MTR, blue) objects

dominate the lower ranks from φ -0.141 to (-0.391). The treated African-type male

spectral (NMTR, turquoise) objects provide no practical information as they are

scattered across the 109 ranks.

245

Legend

Female Treated (FTR) = Pink

Male Treated (MTR) = Blue

African-type Male Treated

(NMTR) = Light Blue

African-type Female Treated

(NFTR) = Green


1 FTR 0.725

2 FTR 0.685

3 NMTR 0.6

4 CFTR1010 0.567

5 FTR 0.509

6 FTR 0.471

7 NFTR 0.461

8 FTR 0.459

9 FTR 0.456

10 FTR 0.438

11 FTR 0.421

12 CFTR1011 0.41

13 FTR 0.396

14 FTR 0.389

15 FTR 0.348

16 NMTR 0.343

17 CFTR102 0.342

18 FTR 0.336

19 NFTR 0.325

20 FTR 0.281

21 MTR 0.265

22 CFTR109 0.261

23 CFTR105 0.249

24 NFTR 0.247

25 NMTR 0.236

26 FTR 0.214

27 FTR 0.2

28 MTR 0.199

29 NFTR 0.198

30 NMTR 0.191

31 MTR 0.164

32 FTR 0.158

33 NFTR 0.147

34 NFTR 0.144

35 CFTR106 0.143

36 MTR 0.132

37 MTR 0.103

38 FTR 0.1

39 FTR 0.09

40 CFTR104 0.081

41 NFTR 0.073

42 FTR 0.071

43 NFTR 0.071

44 FTR 0.063

45 NFTR 0.059

46 FTR 0.053

47 NFTR 0.052

48 CFTR107 0.041

49 NMTR 0.037

50 NFTR 0.033

51 CFTR108 0.023

52 FTR 0.01

53 NFTR 0.002

54 MTR -0.006

55 FTR -0.007


56 MTR -0.007

57 FTR -0.008

58 NFTR -0.01

59 NFTR -0.016

60 NFTR -0.024

61 MTR -0.049

62 NMTR -0.065

63 NFTR -0.065

64 CFTR101 -0.066

65 NFTR -0.118

66 NFTR -0.131

67 FTR -0.135

68 NFTR -0.137

69 MTR -0.141

70 MTR -0.15

71 MTR -0.15

72 MTR -0.166

73 NMTR -0.169

74 MTR -0.179

75 MTR -0.192

76 MTR -0.193

77 NFTR -0.195

78 MTR -0.195

79 MTR -0.204

80 MTR -0.223

81 MTR -0.24

82 MTR -0.24

83 NFTR -0.245

84 MTR -0.249

85 MTR -0.258

86 MTR -0.267

87 NFTR -0.283

88 NFTR -0.287

89 NFTR -0.29

90 MTR -0.308

91 NFTR -0.312

92 MTR -0.322

93 NFTR -0.322

94 MTR -0.327

95 MTR -0.328

96 MTR -0.339

97 MTR -0.351

98 FTR -0.354

99 MTR -0.363

100 MTR -0.368

101 MTR -0.371

102 NFTR -0.373

103 MTR -0.38

104 MTR -0.391

105 NFTR -0.416

106 NFTR -0.475

107 MTR -0.516

108 NMTR -0.543

109 NFTR -0.552

Table 5.12 - PROMETHEE II Net φ Ranking of the Chemically Treated Spectral Database

246

The GAIA bi-plot (Figure 5.21, Δ 73.7 %) shows that the male spectral objects (blue)

have negative scores on PC1 and mostly positive scores on PC2, and are favoured by

the original PC2 criterion. These spectral objects are approximately separated along the

PC2 axis from the female spectral objects (pink, CFTR10 inclusive) which have mostly

negative scores on PC2 and are favoured by the PC1 criterion. These two clusters

mentioned above, are approximately separated from the African-type female and male

(green and turquoise respectively) spectral objects which have mostly positive scores on

PC1 and PC2 and are favoured by the PC3 criterion.

Δ 73.7 %

Figure 5.21 - GAIA analysis of the 109 spectra for the Chemically Treated hair fibre

database; ■ Male mildly treated fibres, ■ Female mildly treated fibres,■ African-type

male, ■ African-type female,, ● pi (Π) decision-making axis, and ■ PC1, PC2 and PC3

criteria.

African-type Female

and Male Treated

PC1

Female Treated

Male Treated

PC2

247

The PCA and Loadings plots (PC2 Loadings) analyses, in association with the second

derivative spectra suggest that the separation of gender – sourced spectra, that male

hair fibres (intensity-wise) prefer, the β-sheet conformation; however, the female hair

fibres displayed more of the α-helical conformation (i.e. Amide II band) in the cuticle

layers. The loadings also illustrate that as a consequence of chemical treatment, there

is a related increase in intensity of aspartic and glutamic acid as shown by the

carboxylate, νa(CO2-), at 1577 cm

-1

5.4 Race: Asian, Caucasian and African-type Hair Fibres

The variability of the morphological, physical and chemical properties of human hair in

each race is greater than the variability of hairs on a single individual‟s head.105

Human

hair can be characterised into three major racial groups (or major population groups)

that include: Caucasoid (principally of European ancestry), African-type (races of

Africa, Melanesia and Papua) and Asian (i.e. Sinetics, Mongols, American Indians and

Eskimos).11 19

The populations of the Indian subcontinent are allied with the European

populations in terms of anthropological kinship and closely allied with the hair type of

the East Asian populations.18

Numerous studies have described the physical differences in hair from people of

different ethnicities.10 11 38 62 64 305-307

Fibre curvature and cross-sectional shape vary

between the three major races, and human scalp hair varies from 40-120 m in

diameter.

Asian hairs have a greater diameter (c.a. 69 – 86 µm; mean 77 µm) with circular cross-

section, are usually straight to wavy in curvature, round to slightly oval, and dark-brown

to black.11 32 66 114

Caucasian hairs have an intermediate diameter (c.a. 67-78 µm; mean 72 µm), are

generally straight to curly in curvature, round to slightly oval in cross-sectional shape

and blonde to dark brown in colour. 32 66 114

248

African-type hair fibres have a high degree of irregularity in diameter (54-85 µm; mean

66 µm); are wavy to woolly, are the most elliptical in cross-sectional shape and brown-

black in colour.11 32 66 114

In terms of chemical composition, the proteins and amino acids of keratin are similar in

African-type, Asian and Caucasian hair.32

Finally, in terms of cuticle thickness, African-type hair is thin whilst Asian hair is thick

and Caucasian hair varies widely. It must be taken into account that FTIR-ATR is a

sample depth dependent technique that monitors the near surface chemistry of samples

only. As African-type hair has the thinnest cuticle of the three races, it is suggested that

the IR evanescent wave may be able to penetrate past the cuticle layer and sample

information from the peripheral area of the cortex which is comprised of α-helical

proteins.18

According to the proposed protocol for analysing single human hair fibres (Figure 4.1,

Section 4.1), the last separation of the spectral objects is on the basis of the major races

mentioned above. In total, there are six scenarios for the three hair classes/types i.e.

male-female untreated, male-female mildly treated and male-female chemically treated.

There is also the possibility of more scenarios if the mildly treated group is sub-divided

into mild physical and mild chemical, and the chemically treated group is sub-divided

into single vs. multiple treatments which in total equals 10 possible scenarios.

However, for this investigation it is not feasible to explore all 10 scenarios because a)

more evidence of the existence of sub-groups must be obtained, and b) some scenarios

(including the theorised new scenarios) did not have enough spectral objects to make

any valid conclusions or deductions. Hence, only two scenarios per gender of the

possible 10 will be analysed.

In previous investigations, Panayiotou22 24

, through the use of PC loadings plots was

able to determine the underlying spectral differences for the discrimination of untreated

Caucasian and Asian FTIR spectra. Asian hair fibres were characterised by the

vibrational bands at 1690 cm-1

(random coil /β-pleated sheet of the Amide I band), with

minor contributions from 1614-1550 cm-1

(β-sheet Amide I band, Tryptophan and

Phenylalanine), 1500 cm-1

(β-pleated sheet Amide II band), 1470-1390 cm-1

and 1470-

249

1390 cm-1

δ(C-H) deformations, and 1310-1225 cm-1

(Amide III band). Caucasian hair

fibres were characterised by the carbonyl stretch ν(C=O) at 1710-1742 cm-1

of the

acidic amino acids and the cystine oxidation spectral region between 1121-1040 cm-1

.

According to Table 1.1, Section 1.2.2.1, (that contrasts the amino acid composition in

human hair fibres), the only significant difference between the major races is that

Caucasian hair has a higher concentration (µmole/gram) of cystine and cysteic acid than

Asian hair.

5.4.1 Racial Spectral differences between Female Hair Fibres

5.4.1.1 Untreated Female Hair Fibres

The 29 untreated female spectra were chosen from the untreated spectral database

(Section 5.3.1.1.), which included the 10 typical untreated reference CFUN No.1

spectra. In total, 89.6 % of the total data variance is retained by the first two PCs with

62.5 % on PC1 and 27.1 % on PC2. This dataset did not include any untreated female

African-type hair spectra, because it is difficult to find such genuinely untreated hair

given the damage caused to the hair by common grooming practices. The PCA scores

plot of the female untreated database is presented in Figure 5.22. With reference to the

CFUN1 samples, Caucasian female spectral objects (blue) have mostly positive scores

on PC1 and are approximately separated along the PC1 axis from the Asian female

spectral objects (pink) which have negative scores on PC1.

250

Figure 5.22 – PCA scores plot of PC1 vs. PC2 of the Untreated Female spectral

database which illustrates the separation of untreated Caucasian female♦ spectra from

untreated Asian female■ spectra on the PC1 axis.

The PC1 loadings plot (Figure 5.23) illustrates that the female Asian spectra (positive

loadings) are characterised by the Amide I and Amide II bands (black) whilst the

Caucasian female bands (negative loadings, including the reference CFUN1 spectra) are

related to the β-sheet of the Amide I (dark blue), νa(CO2-) (green) of aspartic and

glutamic acid and tryptophan (light blue) vibrational bands. This result supports the

suggestion that untreated Caucasian hair is characterised by its higher levels of cystine,

cysteic acid and possibly the amino acid tryptophan (Table 1.1).

-8

-6

-4

-2

0

2

4

6

8

10

12

-20 -15 -10 -5 0 5 10 15

PC1 (62.5%)

PC

2 (

27

.1%

)

Caucasian Female Untreated Asian Female Untreated


Asian Female Untreated

CFUN 1

251

Figure 5.23 – PC1 Loadings plot of the Untreated Female spectral database. The

Amide I and II vibrational bands (positive loadings) correlate to the untreated Asian

female spectral objects whilst the β-sheet, νa(CO2) and Tryptophan bands (negative

loadings) are associated with the untreated Caucasian female spectral objects.

A PROMETHEE II model (Table 5.13) using PC1-PC3 (c.a. 97 % data variance) as

criteria was constructed to provide a quantitative description of the separation of the

female, untreated Asian and Caucasian hair spectra. The PC criteria were minimised,

maximised and minimised respectively in order for the CFUN1 typical untreated

samples to be the reference objects.

Table 5.13 PROMETHEE II Model of the Untreated Female Spectral Database



Minimised/Maximised Minimised Maximised Minimised

p - -

q - -

σ 5.76 3.80 1.90


Weight 1.00 1.00 1.00

252

The PROMETHEE φ ranking (Table 5.14) of the 29 spectra was between φ: +0.546 to

(-0.883) where Caucasian female spectral objects (blue, CFUN1-CFUN110 inclusive)

occupy ranks between φ: +0.546 to (+0.027) and Asian female objects (pink) are the

weaker performing samples between φ = -0.038 to (-0.883). The GAIA bi-plot (Figure

5.24, Δ 73.0 %) shows the approximate PC1 separation of Caucasian and Asian spectral

objects where untreated Caucasian female objects (blue ■) have mostly positive scores

on PC1 and untreated Asian female (pink ■) have negative scores on PC1 and positive

PC2. The original PC1, PC2 and PC3 criteria strongly favour the untreated Caucasian

female samples, CFUN1 reference samples inclusive.

Hence, the loadings analysis of the untreated female IR spectral subset has

demonstrated that Caucasian females have higher levels of the amino acid cystine,

aspartic and glutamic acid. However, it would be essential to compare untreated

African-type female spectra to establish how they are different from Asian and

Caucasian ones.

253

Rank Object φ

1 CFUN13 0.546

2 CFUN15 0.506

3 CF3 0.494

4 CFUN14 0.364

5 CF2 0.36

6 CFUN16 0.309

7 CFUN19 0.304

8 CF1 0.302

9 CFUN18 0.245

10 CFUN17 0.135

11 CFUN12 0.113

12 CF5 0.078

13 CF9 0.071

14 AUN5 0.067

15 CFUN110 0.064

16 CFUN11 0.037

17 CF4 0.027

18 AUN8 -0.038

19 AUN4 -0.099

20 CF18 -0.117

21 AUN3 -0.204

22 CF20 -0.206

23 AUN2 -0.219

24 AUN1 -0.296

25 CF16 -0.345

26 CF17 -0.461

27 AUN7 -0.473

28 AUN9 -0.683

29 AUN6 -0.883

Table 5.14 – PROMETHEE II Net φ Ranking of the Female Untreated Hair Database

Legend

Caucasian Female Untreated (CFUN) = Blue

Asian Female Untreated (AUN) = Pink

254

Δ 73.0 %

Figure 5.24 - GAIA analysis of the 29 spectra for the Untreated Female hair fibre

database; ■ Caucasian Female untreated spectral objects, ■ Asian Female untreated

spectral objects, ● pi (Π) decision-making axis, and ■ Original PC1, PC2 and PC3


PC2

PC1


Asian Female Untreated

255

5.4.1.2 Chemically Treated Female Hair Fibres

The 35 female treated spectra (5 Asian, 25 Caucasian; CFTR10 samples included, and 5

African-type) were removed from the treated dataset (Section 5.3.1.3) and processed by

PCA (Figure 5.25). Unlike the untreated female spectra, three distinct clusters can be

seen along the PC2 axis which relate to the three races. Asian spectral objects (pink)

have positive scores on PC1 and high positive scores on PC2, the treated Caucasian

spectral objects (blue), which contain the typical reference CFTR No. 101-1011 samples

form a cluster that spreads along the centre of the PC1 and PC2 axis with mostly

negative scores on PC2; the treated African-type (green) objects have negative scores

on PC1 and low negative scores on PC2.

Figure 5.25 - PCA scores plot of PC1 vs. PC2 of the Female Treated spectral database

illustrating the segregation of Asian■, Caucasian♦ and African-type▲ spectral objects.

The PC2 loadings plot (Figure 5.26) demonstrates the spectral loadings that

approximately separate treated female Asian spectral objects from treated Caucasian

and African-type ones. The Caucasian and African-type spectral objects (negative

loadings) are described by the β-pleated sheet of the Amide I and Amide II (black)

vibrational bands whilst the Asian spectral objects are related to the anti-symmetric

carboxylate stretch νa(CO2-) of aspartic and glutamic acid (green), tryptophan (blue)

with small loadings from the α-helix (purple) of the Amide II band.

-8

-6

-4

-2

0

2

4

6

8

10

-20 -15 -10 -5 0 5 10 15

PC

2 (

24

.4%

)

PC1 (58.9%)

Caucasian Female Treated Asian Female TreatedAfrican-type Female Treated

CFTR10

Caucasian Female

Treated

Asian Female Treated

African-type Female Treated

256

Figure 5.26 – PC2 Loadings plot of the Female Treated database where the treated

Asian spectral objects (positive loadings) are separated from the treated Caucasian and

African-type spectral objects (negative loadings).

To rank order the 35 spectral objects of the female chemically treated database a

PROMETHEE II model was constructed using PC1, PC2 and PC3 criteria (Table 5.15).

Table 5.15 PROMETHEE II Model of the Chemically Treated Female Spectral

Database



Minimised/Maximised Maximised Minimised Maximised

p - -

q - -

σ 5.89 3.48 2.17


Weight 1.00 1.00 1.00

257

The PROMETHEE II net φ ranking (Table 5.16) was φ +0.467>φ -0.54 where the

African-type female spectral objects were the most preferred samples φ = +0.373 to

(+0.312), followed by the typically treated (CFTR101 – CFTR1011 inclusive)

Caucasian samples φ = +0.287 to (-0.286) and the treated Asian samples dominate the

lower ranks from φ = -0.296 to (-0.50). The GAIA bi-plot (Figure 5.27) depicts the

PROMETHEE II ranking of the spectral objects which illustrates the 2-D separation of

the three races along the PC2 axis, analogous to Figure 5.25. The PC1 and PC3 criteria

favour the Caucasian (blue) spectral objects whilst the PC2 criterion favours the

African-type (green) objects.

The results indicate that female Asian hair fibres are separated from female Caucasian

and African-type hair fibres on the basis of the amino acids tryptophan, aspartic and

glutamic acid.

258


1 CFTR9 0.467

2 CFTR7 0.435

3 CFTR106 0.374

4 NFTR184 0.373

5 CFTR105 0.342

6 NFTR185 0.321

7 NFTR183 0.313

8 NFTR181 0.312

9 CFTR103 0.287

10 CFTR102 0.279

11 CFTR101 0.257

12 CFTR4 0.191

13 CFTR5 0.189

14 CFTR107 0.182

15 NFTR182 0.169

16 CFTR33 0.153

17 CFTR104 0.141

18 CFTR8 0.125

19 CFTR1010 0.104

20 CFTR1011 -0.02

21 CFTR31 -0.101

22 CFTR23 -0.214

23 CFTR108 -0.222

24 CFTR25 -0.246

25 CFTR28 -0.286

26 AFTR221 -0.296

27 CFTR109 -0.308

28 CFTR22 -0.311

29 AFTR222 -0.332

30 CFTR24 -0.359

31 AFTR224 -0.372

32 CFTR26 -0.422

33 AFTR223 -0.486

34 AFTR225 -0.5

35 CFTR29 -0.54

Table 5.16 - PROMETHEE II Net φ Ranking of the Female Chemically Treated Hair

Legend

Caucasian Female Treated (CFTR) = Blue

Asian Female Treated (AFTR) = Pink

African-type Female Treated (NFTR) = Green

259

Δ 80.22 %

Figure 5.27 - GAIA analysis of the 35 spectra for the Chemically Treated Female hair

fibre database; ▲ Caucasian female treated objects, ■ Asian female treated objects,

African-type female objects■, ● pi (Π) decision-making axis, and ■ Original PC1, PC2

and PC3 criteria using a Gaussian preference function.

PC1

PC2

Asian Female Treated

Caucasian Female

Treated

African-type Female

Treated

260

5.4.2 Racial spectral differences between Male Hair Fibre Spectra

5.4.2.1. Mildly Treated Male Hair Fibres

In total, the 92 male mildly treated spectra (41 Asian, 10 Caucasian and 41 African-

type) were removed from the male-female mildly treated spectral database (Section

5.3.1.2) and processed by PCA (Figure 5.28). The African-type spectral objects (green)

form a large cluster with positive scores on PC2 and spread across the PC1 axis. They

are separated on the PC2 axis from the mildly treated Asian (pink) and Caucasian (blue)

spectral objects which have negative scores on PC2. It is difficult to discern a

separation of the Asian and Caucasian spectral objects as the Asian objects form a

cluster which has large variance across the PC1 axis.

Figure 5.28 – PCA scores plot of PC1 vs. PC2 of the Male Mildly Treated spectral

database illustrating the separation of African-type male objects▲ from Asian■ and

Caucasian♦ objects on the PC2 axis.

-8

-6

-4

-2

0

2

4

6

8

-25 -20 -15 -10 -5 0 5 10 15 20

PC

2 (

13

.5%

)

PC1 (67.4%)

Caucasian Male Mildly Treated Asian Male Mildly Treated

African-type Male Mildly Treated

African-type Male Mildly

Treated

Asian + Caucasian Male

Mildly Treated

261

The PC2 loadings plot (Figure 5.29) demonstrates that mildly treated African-type

objects (positive loadings) are influenced by the β-pleated sheet (green) of the Amide I

band, tryptophan (turquoise) and to a lesser degree the α-helix of the Amide II band

(red), whilst the Asian and Caucasian spectral objects are associated with the β-sheet

and random coil of Amide I (black) and to a minor degree the α-helix Amide I (blue).

The spectral objects of the male mildly treated database were rank ordered using a

PROMETHEE II model using PC1-PC3 scores as criteria (Table 5.17).

Figure 5.29 – PC2 Loadings plot of the Male Mildly treated database which illustrates

spectral variables that separate African-type male mildly treated (positive loadings)

from Asian and Caucasian (negative loadings) mildly treated fibres.

262

Table 5.17 PROMETHEE II Model of the Mildly Treated Male Spectral Database



Minimised/Maximised Maximised Maximised Minimised

p - -

q - -

σ 5.98 2.67 2.17


Weight 1.00 1.00 1.00

The PROMETHEE II net φ ranking (Table 5.18) order for the male mildly treated

objects was φ +0.677>φ>-0.917. The African-type spectral objects (NMTR, green)

were the most preferred samples φ = +0.677 – (+0.129) whilst the Asian spectral objects

(AMTR, pink) dominated the middle to lower ranking φ = +0.090 – (-0.917).

Interspersed between the African-type and Asian spectral objects are the mildly treated

Caucasian objects (CMTR, blue) that provide little information as to its actual rank

order of the races. However, it must also be taken into consideration that there are only

10 Caucasian spectral objects.

The GAIA bi-plot (Figure 5.30) indicates the approximate separation of Asian (pink)

and Caucasian (blue) spectral objects from African-type (green) ones on the PC1 axis.

The original PC1 criterion favours the Asian and Caucasian spectral objects, whilst the

original PC2 and PC3 criteria favour the African-type spectral objects.

263

Legend


(NMTR) = Green

Asian Male Treated (AMTR)

= Pink

Caucasian Male Treated

(CMTR) = Blue


1 NMTR 0.677

2 NMTR 0.603

3 NMTR 0.597

4 AMTR 0.593

5 AMTR 0.590

6 NMTR 0.588

7 NMTR 0.576

8 NMTR 0.490

9 NMTR 0.471

10 NMTR 0.449

11 NMTR 0.438

12 NMTR 0.424

13 NMTR 0.409

14 NMTR 0.377

15 NMTR 0.37

16 NMTR 0.368

17 NMTR 0.339

18 NMTR 0.328

19 NMTR 0.302

20 CMTR 0.290

21 NMTR 0.281

22 AMTR 0.280

23 CMTR 0.280

24 NMTR 0.262

25 CMTR 0.252

26 AMTR 0.249

27 AMTR 0.238

28 NMTR 0.226

29 NMTR 0.222

30 NMTR 0.191

31 NMTR 0.179

32 NMTR 0.169

33 AMTR 0.163

34 NMTR 0.158

35 NMTR 0.152

36 NMTR 0.136

37 NMTR 0.129

38 AMTR 0.105

39 NMTR 0.104

40 AMTR 0.090

41 AMTR 0.086

42 CMTR 0.023

43 NMTR 0.015

44 CMTR 0.008

45 AMTR 0.008

46 AMTR 0.003

47 AMTR -0.003


48 AMTR -0.005

49 AMTR -0.007

50 NMTR -0.027

51 AMTR -0.029

52 AMTR -0.062

53 NMTR -0.073

54 AMTR -0.093

55 NMTR -0.102

56 AMTR -0.109

57 AMTR -0.110

58 AMTR -0.118

59 NMTR -0.122

60 NMTR -0.130

61 AMTR -0.141

62 NMTR -0.150

63 CMTR -0.150

64 CMTR -0.165

65 NMTR -0.203

66 AMTR -0.208

67 AMTR -0.241

68 NMTR -0.251

69 CMTR -0.258

70 AMTR -0.265

71 NMTR -0.268

72 AMTR -0.269

73 AMTR -0.272

74 AMTR -0.274

75 AMTR -0.284

76 CMTR -0.304

77 AMTR -0.352

78 NMTR -0.390

79 AMTR -0.400

80 AMTR -0.410

81 AMTR -0.412

82 NMTR -0.420

83 AMTR -0.448

84 AMTR -0.453

85 AMTR -0.489

86 CMTR -0.528

87 AMTR -0.559

88 AMTR -0.576

89 AMTR -0.589

90 AMTR -0.756

91 AMTR -0.895

92 AMTR -0.917

Table 5.18 PROMETHEE II Net φ Ranking of the Male Mildly Treated Hair Database

264

Δ 78.0 %

Figure 5.30 - GAIA analysis of the 92 spectra for the

Male Mildly Treated hair fibre database; ■ Caucasian

male mildly treated objects, ■ Asian male mildly treated

objects, African-type male mildly treated objects■, ● pi

(Π) decision-making axis, and ■ Original PC1, PC2 and


Asian and Caucasian

Male Mildly Treated

African-type Male

Mildly Treated

PC2

PC1

265

5.4.2.2. Chemically Treated Male Hair Fibres

The final scenario involves the analysis of the male chemically treated database which

contains 41 spectra (14 Asian, 9 Caucasian, and 18 African-type) of the total chemically

treated spectral database (Section 5.3.1.3). The PCA scores plot of the male treated

spectral database is presented in Figure 5.31. The scenario is similar to Figure 5.28 of

the male mildly treated database except that Asian (pink) and Caucasian (blue) spectral

objects have positive scores on PC2 whilst African-type (green) spectral objects have

negative scores on PC2. However, the PC2 spectral variables (Figure 5.32) that

separate the hair races are not similar to the Figure 5.29 PC2 loadings plot. The treated

African-type spectral objects (negative loadings) are described by the β-sheet of the

Amide I and Amide II bands (black). The Asian and Caucasian spectral objects

(positive loadings) are mainly associated with the tryptophan (light green) with minor

contributions from the β-sheet and random coil Amide I (dark blue), α-helix Amide I

(light blue), anti-symmetric carboxylate stretch νa(CO2-) of aspartic and glutamic acid

(dark green), and the α-helix of the Amide II band (turquoise).

Figure 5.31 – PCA scores plot of PC1 vs. PC2 of the Male Chemically Treated

Database which illustrates the separation of Asian■ and Caucasian♦ from African-

type▲ spectral objects on the PC2 axis.

-6

-4

-2

0

2

4

6

8

10

12

-15 -10 -5 0 5 10 15 20 25

PC

2 (

13

.4%

)

PC1 (74.3%)

Caucasian Male Treated Asian Male Treated


Asian + Caucasian Male

Treated


266

Figure 5.32 – PC2 Loadings plot of the male treated spectral database illustrating the

variables which separate the Asian and Caucasian (positive loadings) from the African-

type (negative loadings) spectral objects.

Table 5.19 explains the PROMETHEE II model used to rank order the male chemically

treated spectral objects.

Table 5.19 PROMETHEE II Model of the Chemically Treated Male Spectral

Database



Minimised/Maximised Minimised Minimised Maximised

p - -

q - -

σ 6.17 2.68 1.91


Weight 1.00 1.00 1.00

267

Rank Object

Net φ Index

1 NMTR 0.802

2 NMTR 0.643 3 NMTR 0.550

4 NMTR 0.541

5 NMTR 0.4

6 NMTR 0.398

7 AMTR 0.320

8 NMTR 0.245

9 NMTR 0.213

10 AMTR 0.183

11 NMTR 0.168

12 NMTR 0.077

13 NMTR 0.074

14 CMTR 0.067

15 AMTR 0.049

16 CMTR 0.027

17 AMTR 0.016

18 CMTR -0.012

19 NMTR -0.023

20 AMTR -0.037

21 AMTR -0.065

22 AMTR -0.080

23 AMTR -0.091

24 AMTR -0.097

25 CMTR -0.098

26 CMTR -0.099

27 AMTR -0.107

28 AMTR -0.115

29 CMTR -0.135

30 CMTR -0.142

31 AMTR -0.149

32 AMTR -0.196

33 NMTR -0.290

34 NMTR -0.304

35 CMTR -0.322

36 NMTR -0.350

37 CMTR -0.387

38 AMTR -0.387

39 NMTR -0.409

40 NMTR -0.424

41 NMTR -0.449

Table 5.20 PROMETHEE II Net φ Ranking of the Male Chemically Treated Hair Database

Legend

African-type Male Treated (NMTR) = Green

Asian Male Treated (AMTR) = Pink

Caucasian Male Treated (CMTR) = Blue

PC1

268

Δ 76 %

Figure 5.33 - GAIA analysis of the 41 spectra for the Male Chemically

Treated hair fibre database; ■ Caucasian male treated objects, ■ Asian

male treated objects, African-type male treated objects■, ● pi (Π)

decision making axis, and ■ Original PC1, PC2 and PC3 criteria using

a Gaussian preference function.

PC2


Caucasian Male

Treated

Asian

Male

Treated

PC1

269

The PROMETHEE II net φ ranking (Table 5.20) of the chemically treated male spectral

database was φ = 0.802>φ>-0.449. The African-type spectral objects dominated the

upper and lower ranks φ = +0.802 to (+0.074) and φ = -0.290 to (-0.449). The Asian

spectral objects occupied the middle ranking φ = +0.037 to (-0.196) and as observed in

the previous scenario the Caucasian spectral objects were scattered amongst the Asian

spectral objects due to a small population size.

The GAIA bi-plot (Figure 5.33, Δ 76 %) illustrates the approximate separation of the

African-type spectral objects (green) with negative scores on PC1 from the Asian and

Caucasian spectral objects with positive scores on PC1 and PC2. As per the GAIA bi-

plot of the male mildly treated database (Figure 5.30), the PC1 criterion somewhat

favours the Asian and Caucasian spectral objects whilst the PC2 and PC3 criteria favour

the African-type spectral objects. The overall decision axis is in preference of the

African-type spectral objects as according to the setup of the model (Table 5.17).

With male hair fibres, Asian and Caucasian spectra are similar, and are separated

along the PC2 axis from male African-type hair fibres. African-type spectra are

described by the β-pleated sheet of the Amide I band.

270

5.5 Potential Extension of the Forensic Protocol

These studies have demonstrated the necessity for further investigation and extension of

the forensic protocol for the analysis of single human hair fibres, predominately with

the aid of a wider variety of samples. The variety of the samples used in the

investigation did not permit all possible scenarios of the protocol to be analysed.

Further sampling is therefore needed to compensate for the variation of human hair in

our society. This will hopefully allow analysis of FTIR-ATR spectra in each category

of the protocol. Of the male subset of IR spectra, only one sample was classified as

untreated by FC analysis (African-type Male No. 1, NUN1). Of the untreated variety

there were also no African-type female hair fibres available as described by FC and

PCA. In the mildly treated hair class, the female hair subset lacked spectra for distinct

discrimination of the objects. As a result, the protocol remains as a preliminary, yet

developed methodology (Figure 5.34) in comparison to previous investigations.22-24

Furthermore, the results from Section 4.2.1.1 and Section 5.2.2.1 provided adequate

evidence to warrant the sub-division of the mildly treated database into mild physical

treatment (e.g. from grooming, combing, towel drying shampoo and conditioning) and

mild chemical treatment (e.g. photo-oxidative bleaching, swimming in chlorinated

water, use of hair styling products and hair straightening) hair classes. This was

achieved with the utilisation of a 4-cluster FC model. Hence, treatment specific

sampling would be required to analyse and verify that hypothesis. There is also

reasonable data to suggest that the chemically treated spectral objects (Section 5.3.1.3)

can be sub-divided into single versus multiple treatments, especially observed with

African-type female hair, as hair of that type requires a number of cosmetic processes to

achieve the desired straight or permed hair geometry. At the racial level, it may be

possible to further discriminate hair fibres from each race into their respective

ethnic/national groups (i.e. African-type hair spectra – African, Papa New Guinea,

Torres Strait Islands, Samoan, Tongan, etc.).

Therefore, the more that the analysis methodology can be segregated at each interval or

tier in the protocol (i.e. treatment and race), the more accurate and informative the

spectral identifications of unknown hair fibres can become.

271

Untreated Mildly Treated Treated

Male Female Male Female Male Female

Caucasian Asian Caucasian Asian African

African

Caucasian Asian

African

Caucasian Asian

PCA (+FC*)

PCA (+FC*) PCA (+FC*)

Unknown Fibre

Figure 5.34 – Preliminary Forensic Protocol for Analysis of Single Human Hair Fibres by FTIR-ATR Spectroscopy with the aid of Chemometrics.

*FC Classification- Preferred Classification Method (if available)

272


Firstly, before the protocol could be modelled, it was imperative to examine if African-

type hair IR spectra would fit the proposed forensic protocol in both the 1750-800 cm-1

and alternative 1690-1500 cm-1

IR vibrational regions. This had only been attempted in

the previous investigation23

where the results indicated a contradiction to the protocol.

In the current study, when compared with Asian and Caucasian spectra, PCA illustrated

that African-type fibre IR spectra from each hair class (i.e. untreated, mildly treated or

treated) clustered strongly with the respective classes of the Asian and Caucasian in the

1690-1500 cm-1

region, and not in the 1750-800 cm-1

spectral region. This suggested

that when the cystine oxidation region is used for comparison, the levels of cysteic acid

and oxidative intermediates of cystine is much higher in African-type hair than Asian

and Caucasian hair. It was therefore proposed that a low percentage of African-type

hair fibres would be collected in an untreated or virgin state from scenes of crime etc. It

was suggested that because of the crimp of African-type hair fibres, normal grooming

habits tend to be more destructive than on straight to oval shaped hair. This fact is

supported by the literature studies using SEM. Hence, the observation explained the

contradiction that was suggested in the previous investigation23

which showed untreated

African-type spectral objects clustering with treated spectra and vice versa.

The spectra from the three hair classes were then separated into three data sub-sets. The

next separation of the IR spectra for the methodology was on the basis of gender.

Spectral objects of male and female spectra are separated along the PC2 axis. The PC2

loadings plots for each class indicated that the separation of gender is on the basis of the

β-pleated sheet Amide I for male spectra and the α-helix Amide II vibrational band for

female spectra. This supported the observations of the raw and second derivative IR

spectra. In relation to the differences in chemical composition between genders for

untreated, mildly treated and treated fibres, it is hypothesised that female IR spectra

demonstrated strong intensity of the amino acid tryptophan (1554 cm-1

). As a

consequence of treatment of female fibres, there is a concomitant increase in intensity

of the carboxylate, (νa(CO2-) 1577 cm

-1), of the acidic amino acids aspartic and glutamic

acids.

273

The spectra for each hair type of each gender were furthered divided into four smaller

sub-sets. The final separation of the spectra was on the basis of racial origin. Not all

scenarios of the protocol for race (6 scenarios) could be analysed because those subs-

sets had little to no spectra available. With female spectra, Caucasian and African-type

spectra are separated from Asian spectra on the basis of the amino acids tryptophan and

aspartic and glutamic acid. With male hair spectra, Asian and Caucasian spectra are

separated from African-type spectra on the basis of the β-pleated sheet and random coil

of the Amide I vibrational band.

274

6.0 Conclusions and Future Investigations

6.1 Concluding Remarks

This dissertation is arguably the first comprehensive investigation of human scalp hair

fibres by FTIR-ATR spectroscopy supported by chemometrics. The IR spectroscopic

measurements made on a single hair fibre were sampled approximately in the middle of

the hair shaft region. The measurements refer to spectral information collected at a

beam penetration depth of 1.30 – 3.06 µm in the IR range of 1750 - 800 cm-1

in a hair

compressed by the ATR tower. This essentially corresponds to spectral information

being collected from the cuticle or near-cuticle and minimal cortex regions. Human

scalp waste hair fibres were collected from 66 individuals, male and female, of Asian,

Caucasian, African-type; varying in age (6-74); un-weathered or variously treated or

coloured. From these hair fibres, 550 spectra were recorded to build a relatively large

database that covered typical hair samples that could be recovered from scenes of crime.

FTIR-ATR spectroscopy carries a number of advantages over FTIR micro-spectroscopy

used in previous investigations: (i) the technique required less sample preparation

offering greater throughput advantage and is relatively less destructive, (ii) greater

spectral resolution between the vibrational bands and do not suffer from “peak

saturation” or “band saturation”, and (iii) the advance in technology of FTIR-ATR

spectrometers has allowed portable use which permits real-time analysis at crime

scenes. In relation to the study‟s contribution to the field of forensic science, it has

provided a novel methodology to systematically identify and discriminate single

unknown human hair fibres. This proposed protocol can be used as a complementary

technique to the current forensic methods of hair analysis. Before this no protocol

existed. The methodology yields information pertaining to the chemical structure of the

fibre including the presence of cosmetic treatment, its gender, and major racial origin of

the subject.

6.1.1 Conclusions of Chapter 3

The “raw” spectra, spectral subtractions and second derivative spectra were compared to

demonstrate the subtle differences in FTIR-ATR spectra between untreated and

275

cosmetically treated hair, its gender and race origins. SEM images were used as

corroborative evidence to demonstrate the surface topography of untreated and treated

hair. SEM images indicated that the condition of cuticle surface could be of three types:

relatively “untreated” with minor damage as seen with hair having no physical or

chemical treatment, “mildly treated” hair consistent with physical-mechanical damage,

and “treated” hair from the use of cosmetic treatments.

Chemical changes in the form of oxidative damage to the fibre are a consequence of

bleaching, permanent dyeing and permanent waving. Additionally, common physical

processes (such as combing, and straightening) also damage fibres as revealed by SEM

micrographs (Section 3.1.1.2.). For the comparison of untreated and treated hair fibres,

the important IR spectral region consisted of the cystine oxidation bands. The cystine

disulphide cross-links (S=S) are oxidised to cysteic acid (-SO3-) as shown by the

prominent increase in intensity at 1037 cm-1

concurrent with the weaker anti-symmetric

cysteic acid band at 1172 cm-1

, which is actually a shoulder of the Amide III band. The

oxidation bands appear together with the responses from the oxidative intermediate

species such as cystine monoxide (S-S=O) at 1071 cm-1

, cystine dioxide (S=O2) at 1114

cm-1

and cysteine-S-thiosulphate (Bunte Salt) at 1022 cm-1

. At higher wavenumbers,

there is a peak shift of the Amide I band from approximately 1627 cm-1

to a strong,

broad maximum at approximately 1631 cm-1

and a shift of the Amide II band from

1520-1515 cm-1

to 1511 cm-1

This suggested a conformational modification of the

secondary structure of the keratin protein, i.e. α-helix to β-pleated sheet transition.

For gender comparison, the Amide II band is significant for differentiation. In general,

for untreated male fibre spectra, there is a sharp narrow band at approximately

1511 cm-1

, whilst untreated female spectra demonstrated a peak maximum at


. Interestingly, for chemically treated female hair

spectra, the Amide II band becomes narrow and sharp at 1511 cm-1

as per the untreated

male fibre spectra. This observation again indicates a conformational change of the

protein as a consequence of the treatment. Apart from the evidence given by the

difference in conformational structural chemistry, IR difference spectra between

genders within each race were practical to identify the main spectral variables that are

consistent for each gender. Female spectra exhibited greater intensity of the amino acid

tryptophan at 1554 cm-1

and aspartic and glutamic acid, ν(CO2-) at 1577 cm

-1.

276

Therefore, to support the hypothesis of a conformational change in the protein structure

(α-helix to β-sheet) which apparently occurs as a result of chemical treatment, second

derivative (derivative spectroscopy) analyses were performed. The results illustrated

that male hair exhibit a strong intensity of the β-sheet conformation (1511 cm-1

) in the

Amide II band whereas female hair spectra exhibited more intense α-helical

conformation spectral pattern (1515-1520 cm-1

).

The more intense β-pleated sheet bands in the male hair spectra suggested that the

cuticle is comprised of a rather amorphous matrix as opposed to a fibrous α-helical

matrix that makes up the cortical cells. This inference is supported by the literature163

where it has been reported that the cuticle has a higher proportion of cystine, proline,

serine, and valine residues that have generally been considered as non-helical forming

amino acid residues.

6.1.2 Conclusions of Chapter 4

The main objective of the research was concerned with the expansion and

diversification of the provisional, unverified Forensic Protocol for hair fibre analyses

over the 1700-850 cm-1

region. To achieve this, a relatively large database of spectra

was required that covered hair samples of different racial backgrounds and treatment

types. Previous investigations only used methodologies based on Asian and Caucasian

hair spectra. In the penultimate study to this one23

, African-type hair spectra

highlighted a contradiction of the protocol concerning the separation of spectra on the

basis of treatment. In the present work, African-type hair spectra were also initially

removed from the preliminary model because of classification or borderline ambiguity

between untreated and treated fibres. To eliminate any uncertainty of multiple class

membership, Fuzzy Clustering (a non-parametric classification method) was employed

as an unbiased test for other class membership and multiple class belongings of objects.

A 3-cluster model was calculated to allow for another hair fibre class. There was

immediate evidence that a third class of fibre existed. This hair fibre class was

categorised as the Mildly Treated fibre group. The remaining „fuzzy‟ or misclassified

objects were removed from the database. Pattern recognition (PCA) illustrated that a

third fibre group existed. This group consisted of spectra from hairs that had been

subjected to mild forms of physical and/or chemical treatment. MCDM (quantitative

277

object ranking order) showed that the groups would be quantitatively separated. Upon

further examination using a 4-cluster FC model there was some evidence that the mildly

treated group could potentially be separated into mild physical and mild chemical

treatments.

Based on the above reduction of the data matrix after exclusion of the „fuzzy objects‟, a

new aim was proposed to analyse the spectra in the 1690-1200 cm-1

region. FC 4-

cluster modelling showed that the mildly treated group could be further sub-divided into

mild physical treatment and mild chemical treatment. Ultimately, the most appropriate

region for analysing the FTIR-ATR hair keratin spectra, which gave the least number of

“fuzzy samples” was found to be the 1690-1500 cm-1

IR wavenumber region which

contained principally the Amide I and II absorption bands.

6.1.3 Conclusions to Chapter 5

The global perspective and rationale of this investigation endeavoured to provide

analysts with a rapid methodology (i.e. Forensic Protocol) for analysing single unknown

human hair fibres via FTIR-ATR Spectroscopy coupled with Chemometrics as a

complementary technique to the current methods. Initially, African-type hair fibre

spectra were processed using the proposed 1690 -1500 cm-1

spectral region which is

novel for the development of the Forensic Protocol. It now appears that in the previous

work23

where Forensic Protocol ambiguities were apparent, the inclusion of the cystine

oxidation region in the spectral range (1750 – 800 cm-1

) confused the spectral

classification because of the chemical inconsistency. It appears that this chemical

inconsistency arises from the composition of the oxidised products from „cystine‟ and is

reflected between 1200-1000 cm-1

. This is particularly so with the African-type hair

samples which are robust in the 1690-1500 cm-1

range because the discrimination of the

spectra is reliant on the change in conformation (α-helical to β-pleated sheet and/or

random coil) of the fibre.

On the basis of the separation of gender – sourced spectra, the PC2 loadings plots for

the untreated, mildly treated and chemically treated hair fibres suggest that male hair

fibres exhibit more (intensity-wise) of, or prefer, the β-sheet conformation; however, the

female hair fibres displayed more of the α-helical conformation (i.e. Amide II band) in

278

the cuticle layers. In terms of amino acids, it is suggested that female spectra are defined

by greater intensity of the amino acids tryptophan (1554 cm-1

), aspartic and glutamic

acid (νa(CO2-) 1577 cm

-1). These inferences are both supported by the IR spectral

evidence (derivative and difference spectra) from chapter 3.

For the separation of samples based on racial differences, untreated Caucasian hair is

discriminated from Asian hair as a result of having higher levels (µmole/gram) of the

amino acid cystine and cysteic acid. Due to the common grooming habits of African-

type hair, no untreated fibres were available for comparison, as demonstrated by FC

modelling. However, in mildly or chemically treated hair fibres, Asian and Caucasian

hair fibres are similar, whereas African-type fibres are relatively different as illustrated

by the separation on the PCA scores plot (Figure 5.28 and Figure 5.31). It is suggested

that the difference is based on the geometry of the hair. Caucasian and Asian have

straight to elliptical shaped hair, whilst African-type hair have a highly curled geometry.

Of the mildly treated and chemically treated databases especially, 34 % and 66 %

respectively of the African-type hair IR spectra were misclassified by the 2 class FC

model. These spectra cannot be outliers in a 2-class model. From previous

investigations, it is suggested that the spectra belong to another class of fibre known as

multiply treated hair, mainly seen in some African women. This is a result of a

multitude of treatments to acquire straight or permed hair geometry. Furthermore, if

permanent colouring is also involved then the amount of cysteic acid is further

increased.

The conclusions described in this investigation have furthered the scientific

understanding pertaining to the structural chemistry of human hair fibres. Structural

elucidation FTIR-ATR spectroscopy and Chemometric analysis has facilitated the

development of a novel protocol to analyse unknown single human hair fibres proposed

for viable forensic purpose. The protocol has been modelled in such a way so that the

hair fibre is analysed in three logical, systematic steps i.e. treatment, gender and racial

origin. Advances in FTIR-ATR technology has made it possible for on-site, real-time

analyses.

279

6.2 Future Investigations

In general, the main outcome of this investigation has allowed for the modelling of a

proposed protocol, with the purpose of identifying and gaining information about the

origins of unknown or suspect human hair fibres which can complement the current

forensic methods of hair analysis. Human hair fibres are commonly found at crime

scenes or associated suspect/s. The problem is that crime scenes are not ideal and that

fibres are found in a wide variety of circumstances from effects from types of chemical

treatment or environmental weathering, racial origin and mixed origins. The database

of IR spectra used to build the forensic protocol in this investigation did not allow

analysis of all scenarios. That is why crime authorities e.g. Federal Bureau of

Investigation (FBI) constantly update their databases of DNA and fingerprints of

criminals/suspects.308

Therefore warranting future studies within this topic:

(a) A wider variety of hair fibre sampling is needed to compensate for the

variation of human hair in our society. In addition, a larger number of hairs

per donor are needed to give a better understanding of inter and intra

individual variation. Also, to conduct trials where individuals hair has been

subjected to specific cosmetic treatment regimes. This will hopefully allow

analysis of FTIR-ATR spectra in each category of the protocol.

(b) With respect to the donated African-type hair samples from 23 persons in

this investigation, the protocol indicated (with the exception of the samples

from African-type male No. 1, NUN1), that there were no male or female

untreated African-type hair fibres, only those of the Mildly Treated and

Chemically Treated classes. Therefore, this suggests that upon hair

sampling, the possibility of classifying an African-type hair fibre in the

“untreated” state would be low. Furthermore, with the Asian and Caucasian

male hair samples, the FC modelling highlighted there were no untreated

hair fibres. Hence, to reinforce the inference that male hair fibres are seldom

280

in an untreated state, another randomly sampled set of alleged untreated

male hairs would be required for FTIR-ATR analysis.

(c) The preliminary results in Chapter 4 indicated that the Mildly Treated group

has the potential to be sub-divided into mild physical and mild chemical

treatments using a 4-cluster FC model. To validate that hypothesis,

treatment specific sampling in those classes would be imperative. In terms

of analysis, it would be necessary to explore the use of more PC‟s rather than

the first two PC1 and PC2, which only provided a 2-dimensional trend

across the PC1 axis. Conceivably, the use of the third, fourth, PC etc., may

draw out more data variance and with the aid of PROMETHEE II net φ

flows, the objects can be ordered into distinct classes. The sub-division of

the mildly treated group can allow the identification process to be more

accurate, rather than creating an inaccurate hypothesis of the chemical state

of the fibre. At the racial level of the protocol, it could be favourable to

explore the separation of spectra of the same treatment and gender into

spectra of the same ethnicity (i.e. Indian-Pakistani-Bangladesh etc. vs.

Chinese-Japanese-Korean etc.).

(d) To explore the hypothesis that female hair fibres have a greater

concentration of the amino acid tryptophan over male fibres, it would be

necessary to perform a hydrolysis of the fibres in a strong acid to free the

amino acids and subsequently separate and analyse using HPLC.309

(e) The database for this project was concerned with hair keratin spectra

sampled at the shaft (middle) of the fibre only. Previous work has suggested

that a hair fibre can be classified according to section i.e., root, middle and

tip. Therefore it would be essential to build a comprehensive database which

covers all three sections of the fibre because fibres collected at crime scenes

could be in fragment form. This could be achieved by a specific comparison

of the surface vs. internal chemistry using cross sections of hairs.

(f) Additionally in reference to the spectral database of keratin IR spectra, it

may be necessary to sample and acquire spectra from many fibres from one

281

individual as each hair on a person‟s scalp is not uniformly treated or

weathered by the environment or through grooming.

(g) To experiment with blind trials to test how accurate FTIR-ATR

chemometrics is in determining treatment history, gender and race.

282

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299

Appendix I – Data on Subjects - Forensic Protocol

ETHNICITY GENDER AGE COSMETIC

TREATMENS

SUN

EXPOSURE

SWIMMING

1. Asian (A) Female 42

Semi-Permanent

Dye, Hair Spray Average None

2. Asian Female 21 None Minimum Average

3. Asian Female 42 Semi-Permanent

Dye

Medium None

4. Asian Female 21 None Minimum Average

5. Asian Male 23 Hair Gel, Wax Maximum Average

6. Asian Male 21 None Medium None

7. Asian Female 35 None Minimum None


9. Asian Male 30 Mustard Oil, Hair

Gel

Minimum Minimum

10. Asian Male 22 None Minimum None

11. Asian Female 21 Semi-Permanent

Dye

Medium Minimum

12. Asian Male 19 Fixation Gel Minimum Minimum

13. Asian Male 31 Hair Gel Medium Minimum

14. Asian Male 22 Hair Gel Minimum None

15. Asian Male 26 Herbal Hair Oil Minimum Minimum

16. Asian Female 20

Permanent and

Semi-Permanent

Dye, Frosting Minimum None



19. Asian Male 22 Hair Tonic Average Minimum

20. Asian Male 23 None Medium Minimum

21. Asian Female 25 Moisturiser Average Nil

22. Asian Female 21

Tinged

Hairspray

Wax Average Average

1. Caucasian (C) Female 22 None Average None

2.Caucasian Female 23 None Average None

3. Caucasian Male 19 None Minimum Minimum

4. Caucasian Male 23 None Maximum None

300

5. Caucasian Male 54 None

(Greying)

Medium None

6. Caucasian Male 51 None

(Greying)

Minimum None

7.Caucasian Male 54 None

(Greying)

Minimum None

9. Caucasian Female 53

Bleached

Semi-Permanent

Dye Maximum Minimum

10. Caucasian

Female 53 Bleached, Dyed Minimum Maximum

11.Caucasian Female 21 Semi-Permanently

Dyed

Medium Medium

12. Caucasian Female 18 Permanently

Dyed, Hair Spray

and Wax

Medium None

13. Caucasian Female 23

Bleached, Semi

and Permanently

Dyed Average None

14. Caucasian Female 22 Foils, Semi-

Permanently Dyed

Medium Minimum

15. Caucasian Female 21 Foils Minimum Minimum

16. Caucasian Female 74 Mousse Minimum Minimum

17. Caucasian Female 21 Permanently

Dyed, Bleached,

Gel, Hair Spray,

Wax

Medium Medium

18. Caucasian

Female 21 Foils Average None

19. Caucasian

Male 18 Hair Gel Medium Medium

20. Caucasian Female 18 Perm, Hair Gel Average Minimum

21. Caucasian

Male 20

Bleached

Permanent Dye

Hair Gel

Hair spray Average Minimum

1. African-type

(N) Male 24 None Maximum None

2. African-type Male 22 None Minimum Minimum

301

3. African-type Male 22 None Minimal Minimal

4. African-type Female 24

Straightened, Hair

Spray, Moisturiser Average None

5. African-type Female 29

Perm, Permanent

Dye, Hairspray Minimum None

6. African-type Male 18 Moisturiser Minimum None

7. African-type Male 36

Permanent Dye,

Hair Gel Average Minimum

8. African-type

Male 22 None Minimum Minimum

9. African-type

Male 46

Permanently Dyed

Hair Cream Minimum Minimum

10. African-type

Male 10 Hair Cream Minimum Medium

11. African-type

Male 46 None Maximum Minimum

12. African-type


13. African-type

Male 13 Relaxed Average Medium

14. African-type


15. African-type


16. African-type

Male 42 None Average Minimum

17. African-type

Male 15 None Average Medium

18. African-type

Female 41

Permanently

Waved

Relaxed

Hair Cream Minimum Minimum

19. African-type

Female 47

Semi-Permanently

Dyed Average Minimum

20. African-type Female 37 None Average Minimum

21. African-type

Female 6 None Average Medium

22. African-type

Female 14

Permanently

Waved

Relaxed

Hair Cream Minimum Medium

23. African-type

Female 16 Relaxed Average Medium

302

Appendix I (Continued) - Hair Profile Survey for Forensic

Investigation on the Effects of Environmental Stress on Single α-

Keratin Human Hair Fibres, PhD Thesis, Queensland University of

Technology

(Please provide at least 10 strands of Hair, Thank You)

(Please circle or fill in appropriate space provided)

(General information below is needed as it may aid in the interpretation process)

Gender: Male Female

Age: ____________

Ethnicity/Origin: _______________________

Chemical Treatment/s (e.g. Bleached, Highlights, Tinged, Semi or Permanently Dyed,

Waved, Permanently Waved, Gels, Wax, Hair Spray, etc.):

Level of Sun Exposure: Minimal Moderate Maximum

Swimming Frequency: Minimal Moderate Maximum

Smoker: Yes No

Medication/s (Do not list): Yes No

303

Appendix II – Fuzzy Clustering (p = 1.2) 3-cluster model 1750-800 cm-1

Spectra Class

1 Class

2 Class

3

AAF121 0.032 0.968 0

AAF122 0.997 0.003 0

AAF123 0.046 0 0.954

AAF171 0.999 0.001 0

AAF172 1 0 0

AAF173 0.908 0.092 0

AAF2121 0.998 0.002 0

AAF2171 0.542 0.458 0

AAF2172 0.999 0 0.001

AAF3121 0.557 0.443 0

ACF11 0.259 0.741 0

ACF111 0.988 0.012 0

ACF12 0.903 0.097 0

ACF21 0.997 0.001 0.002

ACF211 0 0 1

ACF2111 0 1 0

ACF212 0 0 1

ACF213 0.978 0.022 0

ACF22 0.892 0.108 0

ACF221 0.02 0 0.98

ACF222 0.985 0.015 0

ACF23 0.002 0 0.998

ACF241 0 0 1

ACF242 0 0 1

ACF261 0 0 1

ACF262 1 0 0

ACF291 1 0 0

ACF292 1 0 0

ACF3111 1 0 0

ACF41 0 0 1

ACF4111 0.177 0.822 0.001

ACF42 0 1 0

ACF43 0 0 1

ACF5111 0.873 0.127 0

ACF61 0 0 1

ACF62 0 0 1

ACF63 1 0 0

ACF91 0.013 0.987 0

ACF92 1 0 0

ACF93 0.002 0 0.998

ACM101 0.005 0.995 0

ACM102 0.995 0.001 0.004

ACM131 0.999 0.001 0

ACM132 1 0 0

ACM133 0.042 0.958 0

ACM191 0.992 0.001 0.007

ACM192 0 1 0

ACM201 0 0 1

ACM202 0.861 0 0.139

ACM203 1 0 0

ACM2101 0.998 0.001 0

Spectra Class

1 Class

2 Class

3

ACM2131 0.935 0.065 0

ACM2132 1 0 0

ACM2191 0 1 0

ACM2201 1 0 0

ACM2202 0.001 0 0.999

ACM3101 0.001 0.999 0

ACM3191 0 1 0

ACM4101 0.979 0.003 0.018

AF111 0 0 1

AF112 0.374 0 0.626

AF113 0.856 0 0.144

AF2111 0.981 0 0.019

AF2112 1 0 0

AF2203 0.005 0 0.994

AF2281 0.371 0 0.628

AF2282 0 1 0

AF2283 1 0 0

AF2284 0 0 1

AF241 0.001 0 0.999

AF242 0.001 0 0.999

AF243 0 0 1

AF244 0 0 1

AF245 0.002 0 0.998

AF281 0.835 0 0.165

AF282 1 0 0

AF283 0.055 0 0.945

AF284 0.989 0 0.011

AF3281 0.001 0.999 0

AF3282 0.019 0.981 0

AF3283 0 1 0

AM151 0 0 1

AM152 0 0 1

AM153 1 0 0

AM201 0.001 0.999 0

AM2201 0 1 0

AM2251 0.931 0.069 0

AM2252 0.011 0 0.989

AM2253 1 0 0

AM2261 1 0 0

AM2262 0.995 0.005 0

AM2263 0.997 0.003 0

AM2271 0.999 0 0.001

AM2272 1 0 0

AM2273 1 0 0

AM2281 1 0 0

AM2282 1 0 0

AM2283 1 0 0

AM2284 1 0 0

AM251 0.034 0 0.966

AM252 0.991 0 0.008

AM253 1 0 0

304

Appendix II - Continued

Spectra Class

1 Class

2 Class

3

CF2305 0.089 0 0.911

CF2311 0.996 0.004 0

CF2312 0 0 1

CF2313 0.084 0.916 0

CF2314 1 0 0

CF2315 1 0 0

CF2321 0 1 0

CF2322 0.999 0.001 0

CF241 0.262 0.735 0.002

CF242 0.001 0.999 0

CF243 0.599 0.218 0.183

CF244 0.593 0.362 0.045

CF245 0.662 0.297 0.04

CF11 0 0 1

CF12 0 0 1

CF13 0 0 1

CF14 0 0 1

CF15 0 0 1

CF291 1 0 0

CF292 0.999 0.001 0

CF293 0.067 0 0.933

CF294 0.924 0.076 0

CF295 1 0 0

CF301 1 0 0

CF302 0.002 0 0.998

CF303 1 0 0

CF304 0.98 0 0.02

CF305 1 0 0

CF311 0 0 1

CF312 0.001 0 0.999

CF313 0 0 1

CF314 0.992 0 0.008

CF315 1 0 0

CF321 0 1 0

CF322 0 1 0

CF3321 0.088 0.912 0

CF3322 0.001 0.999 0

CF41 0.058 0.941 0

CF42 0.05 0.949 0.001

CF43 0.319 0.658 0.023

CF44 0.063 0.937 0.001

CF45 0.002 0.998 0

CF101 0.055 0.945 0

CF102 0.999 0.001 0

CF103 1 0 0

CF104 0.001 0.999 0

CF105 0.186 0.814 0

CF106 0.983 0.017 0

CF107 0 1 0

CF108 0 1 0

CF109 0 1 0

Spectra Class

1 Class

2 Class

3

AM261 0.998 0 0.002

AM262 1 0 0

AM263 0.998 0.002 0

AM271 0.006 0.994 0

AM281 1 0 0

AM282 1 0 0

AM283 0 0 1

AM284 0.839 0 0.16

AM285 1 0 0

AM3201 0 1 0

AM3202 0.085 0.915 0

AM3251 0 0 1

AM3252 1 0 0

AM3253 0.78 0 0.22

AM3261 0.001 0.999 0

AM3262 1 0 0

AM3263 1 0 0

AM3271 1 0 0

AM3272 0 1 0

AM3273 1 0 0

AM4201 1 0 0

AM4251 1 0 0

AM4252 1 0 0

AM4253 1 0 0

AM4271 1 0 0

AM4272 0.005 0 0.995

AM4273 0 0 1

CF161 0 0 1

CF162 0.001 0 0.999

CF211 0.024 0 0.976

CF212 1 0 0

CF213 0 0 1

CF2161 0 0 1

CF2162 0 0 1

CF2163 0 0 1

CF2211 0.001 0.999 0

CF2212 1 0 0

CF16 0 0 1

CF17 0.001 0 0.999

CF18 0 0 1

CF18 0 0 1

CF110 0 0 1

CF2291 1 0 0

CF2292 1 0 0

CF2293 0.775 0 0.225

CF2294 0.01 0 0.99

CF2295 0.686 0.001 0.313

CF2301 0.128 0 0.872

CF2302 0 1 0

CF2303 0 0 1

CF2304 0.025 0 0.975

305

Appendix II - Continued

Legend

A = Asian

C = Caucasian

F = Female

N = African-type

M = Male

Untreated = Blue

Mildly Treated = Green

Chemically Treated = Pink

Fuzzy Objects = Blank

Mild Physical = Turquoise

Spectra Class

1 Class

2 Class

3

CF1010 0.982 0.018 0

CF1011 0.997 0.003 0

CFA251 1 0 0

CFA252 1 0 0

CFA253 0.255 0.745 0

CFA254 0.016 0.984 0

CM121 1 0 0

CM122 0.905 0.095 0

CM131 0.992 0 0.008

CM132 0.001 0.999 0

CM133 0.872 0.128 0

CM2121 1 0 0

CM2122 1 0 0

CM2131 0.999 0 0.001

CM2132 1 0 0

CM231 0.989 0.011 0

CM232 1 0 0

CM241 1 0 0

CM251 0 0 1

CM252 0.98 0 0.02

CM253 0.984 0.016 0

CM31 0 1 0

CM3121 1 0 0

CM32 1 0 0

CM33 0 1 0

CM341 0 1 0

CM342 0.969 0 0.031

CM351 1 0 0

CM352 0.999 0 0.001

CM353 0 1 0

CM41 0.001 0.999 0

CM42 0.91 0.09 0

CM441 0 1 0

CM51 0.998 0.002 0

CM52 0.996 0.004 0

CM53 0.995 0.005 0

CM541 0 1 0

CM542 0 1 0

AI3221 0.001 0.999 0

AIF171 0.058 0.941 0

AIF172 0.05 0.949 0.001

AIF173 0.319 0.658 0.023

AIF174 0.063 0.937 0.001

AIF175 0.002 0.998 0

AIF2171 0.055 0.945 0

AIF2172 0.999 0.001 0

AIF2173 1 0 0

AIF2174 0.001 0.999 0

AIF2175 0.186 0.814 0

AIF251 0.983 0.017 0

AIF252 0 1 0

Spectra Class

1 Class

2 Class

3

AIF311 0 1 0

AIF312 0 1 0

AIF313 0.982 0.018 0

AIF315 0.997 0.003 0

AIF51 1 0 0

AIF52 1 0 0

AIF53 0.255 0.745 0

AIM211 0.016 0.984 0

AIM212 1 0 0

AIM221 0.905 0.095 0

AIM2211 0.992 0 0.008

AIM2212 0.001 0.999 0

AIM222 0.872 0.128 0

AIM2221 1 0 0

AIM2222 1 0 0

AIM2223 0.999 0 0.001

AIM223 1 0 0

AIM2291 0.989 0.011 0

AIM2292 1 0 0

AIM2293 1 0 0

AIM2311 0 0 1

AIM2312 0.98 0 0.02

AIM2313 0.984 0.016 0

AIM2314 0 1 0

AIM2315 1 0 0

AIM291 1 0 0

AIM292 0 1 0

AIM293 0 1 0

AIM311 0.969 0 0.031

AIM312 1 0 0

AIM313 0.999 0 0.001

AIM314 0 1 0

AIM3222 0.001 0.999 0

AIM3223 0.91 0.09 0

AIM3291 0 1 0

AIM3292 0.998 0.002 0

AIM3293 0.996 0.004 0

306

Appendix III–Fuzzy Clustering (p = 1.2) 4-cluster Model 1750-800 cm-1

Spectra Class

1 Class

2 Class

3 Class

4

AAF121 0 0 0 1

AAF122 0.001 0 0 0.999

AAF123 0.999 0 0.001 0

AAF171 0.022 0 0 0.977

AAF172 0.762 0 0 0.238

AAF173 0.002 0.003 0 0.995

AAF2121 0 0 0 1

AAF2171 0 0 0 1

AAF2172 0.989 0 0 0.011

AAF3121 0 0 0 1

ACF11 0 0.002 0 0.998

ACF111 0.001 0 0 0.998

ACF12 0 0 0 1

ACF21 0.87 0 0 0.13

ACF211 0.002 0 0.998 0

ACF2111 0.001 0.822 0 0.177

ACF212 0.001 0 0.999 0

ACF213 0 0 0 1

ACF22 0.001 0 0 0.999

ACF221 0.858 0 0.138 0.004

ACF222 0.002 0 0 0.998

ACF23 0.179 0 0.819 0.002

ACF241 0 0 1 0

ACF242 0 0 1 0

ACF261 0 0 1 0

ACF262 0.016 0 0 0.984

ACF291 0.005 0 0 0.995

ACF292 0.199 0 0 0.801

ACF3111 0.397 0 0 0.603

ACF41 0.121 0 0.879 0

ACF4111 0.08 0.302 0.002 0.617

ACF42 0 0.994 0 0.006

ACF43 0.005 0 0.995 0

ACF5111 0.001 0 0 0.999

ACF61 0 0 1 0

ACF62 0 0 1 0

ACF63 0.724 0 0 0.276

ACF91 0 0.068 0 0.932

ACF92 0.997 0 0 0.003

ACF93 0.945 0 0.055 0

ACM101 0.002 0.106 0 0.892

ACM102 0.884 0.001 0.002 0.113

ACM131 0.001 0 0 0.999

ACM132 0.005 0 0 0.995

ACM133 0 0.058 0 0.942

ACM191 0.961 0 0 0.039

ACM192 0 0.97 0 0.03

ACM201 0.005 0 0.995 0

ACM202 1 0 0 0

ACM203 1 0 0 0

ACM2101 0.48 0.004 0.001 0.515

Spectra Class

1 Class

2 Class

3 Class

4

ACM2131 0 0 0 0.999

ACM2132 0.974 0 0 0.026

ACM2191 0 1 0 0

ACM2201 0.156 0 0 0.844

ACM2202 0.829 0 0.17 0

ACM3101 0 0.999 0 0.001

ACM3191 0 1 0 0

ACM4101 0.848 0.001 0.007 0.144

AF111 0.008 0 0.992 0

AF112 1 0 0 0

AF113 1 0 0 0

AF2111 0.996 0 0.001 0.004

AF2112 0.999 0 0 0.001

AF2203 0.023 0 0.975 0.002

AF2281 1 0 0 0

AF2282 0 1 0 0

AF2283 0.999 0 0 0.001

AF2284 0 0 1 0

AF241 0.001 0 0.999 0

AF242 0.001 0 0.999 0

AF243 0 0 1 0

AF244 0 0 1 0

AF245 0.003 0 0.997 0

AF281 1 0 0 0

AF282 0.987 0 0 0.013

AF283 0.998 0 0.002 0

AF284 0.998 0 0 0.002

AF3281 0 0.775 0 0.225

AF3282 0 0.004 0 0.995

AF3283 0 1 0 0

AM151 0.008 0 0.992 0

AM152 0.002 0 0.998 0

AM153 0.037 0 0 0.963

AM201 0 0.999 0 0.001

AM2201 0 0.999 0 0.001

AM2251 0.001 0.001 0 0.998

AM2252 0.607 0 0.39 0.003

AM2253 0.597 0 0 0.403

AM2261 1 0 0 0

AM2262 0.006 0.001 0 0.993

AM2263 0.001 0 0 0.999

AM2271 1 0 0 0

AM2272 0.09 0 0 0.91

AM2273 0.9 0 0 0.1

AM2281 0.555 0 0 0.445

AM2282 0.554 0 0 0.446

AM2283 0.164 0 0 0.836

AM2284 0.009 0 0 0.991

AM251 0.952 0 0.047 0.001

AM252 0.994 0 0 0.005

AM253 0.2 0.001 0 0.8

307

Appendix III – Continued

Spectra Class

1 Class

2 Class

3 Class

4

AM261 1 0 0 0

AM262 0 0 0 1

AM263 0.002 0 0 0.998

AM271 0 0.609 0 0.39

AM281 1 0 0 0

AM282 1 0 0 0

AM283 0.163 0 0.836 0

AM284 0.998 0 0.001 0.001

AM285 0.008 0 0 0.992

AM3201 0 1 0 0

AM3202 0 0.036 0 0.964

AM3251 0.086 0 0.913 0

AM3252 0.184 0 0 0.815

AM3253 0.996 0 0.002 0.002

AM3261 0 0.986 0 0.014

AM3262 0.008 0 0 0.992

AM3263 0.051 0 0 0.949

AM3271 0.975 0 0 0.025

AM3272 0 1 0 0

AM3273 0.871 0 0 0.129

AM4201 1 0 0 0

AM4251 0.993 0 0 0.007

AM4252 0.971 0 0 0.029

AM4253 0.788 0 0 0.212

AM4271 0.996 0 0 0.004

AM4272 0.807 0 0.193 0.001

AM4273 0.002 0 0.998 0

CF161 0 0 1 0

CF162 0.011 0 0.989 0

CF211 0.997 0 0.003 0

CF212 1 0 0 0

CF213 0.285 0 0.715 0

CF2161 0.004 0 0.996 0

CF2162 0 0 1 0

CF2163 0.057 0 0.942 0

CF2211 0 0.86 0 0.14

CF2212 0.54 0 0 0.46

CF16 0 0 1 0

CF17 0.834 0 0.166 0

CF18 0 0 1 0

CF19 0 0 1 0

CF110 0.238 0 0.762 0

CF2291 0.282 0 0 0.718

CF2292 0.324 0 0 0.676

CF2293 0.999 0 0 0

CF2294 0.802 0 0.196 0.002

CF2295 0.996 0 0.002 0.002

CF2301 1 0 0 0

CF2302 0 1 0 0

CF2303 0 0 1 0

CF2304 0.993 0 0.007 0

Spectra Class

1 Class

2 Class

3 Class

4

CF2305 0.999 0 0.001 0

CF2311 0.006 0.001 0 0.993

CF2312 0.301 0 0.699 0

CF2313 0 0.076 0 0.923

CF2314 1 0 0 0

CF2315 0.939 0 0 0.061

CF2321 0 1 0 0

CF2322 0 0 0 1

CF241 0.072 0.098 0.001 0.829

CF242 0.001 0.972 0 0.028

CF243 0.456 0.045 0.062 0.437

CF244 0.3 0.062 0.014 0.624

CF245 0.305 0.043 0.011 0.641

CF11 0.001 0 0.999 0

CF12 0 0 1 0

CF13 0 0 1 0

CF14 0 0 1 0

CF15 0 0 1 0

CF291 0.965 0 0 0.035

CF292 0 0 0 1

CF293 0.967 0 0.031 0.002

CF294 0 0 0 1

CF295 0.018 0 0 0.982

CF301 0 0 0 1

CF302 0.938 0 0.061 0

CF303 0.002 0 0 0.998

CF304 1 0 0 0

CF305 0.035 0 0 0.965

CF311 0 0 1 0

CF312 0.001 0 0.999 0

CF313 0 0 1 0

CF314 1 0 0 0

CF315 1 0 0 0

CF321 0 0.964 0 0.036

CF322 0 0.993 0 0.007

CF3321 0 0.012 0 0.988

CF3322 0 0.807 0 0.192

CF41 0.033 0.29 0 0.676

CF42 0.03 0.484 0.001 0.485

CF43 0.181 0.194 0.011 0.613

CF44 0.034 0.377 0.001 0.588

CF45 0 0.989 0 0.011

CF101 0 0 0 0.999

CF102 0.011 0 0 0.989

CF103 0.023 0 0 0.977

CF104 0 0.012 0 0.988

CF105 0 0 0 1

CF106 0 0 0 1

CF107 0 1 0 0

CF108 0 1 0 0

CF109 0 1 0 0

308

Appendix III - Continued

Spectra Class

1 Class

2 Class

3 Class

4

CF1010 0 0 0 1

CF1011 0 0 0 1

CFA251 0.936 0 0 0.064

CFA252 0.987 0 0 0.013

CFA253 0.004 0.009 0 0.987

CFA254 0.006 0.244 0 0.75

CM121 0 0 0 1

CM122 0.001 0.002 0 0.998

CM131 1 0 0 0

CM132 0 0.526 0 0.474

CM133 0.013 0.027 0 0.959

CM2121 0.935 0 0 0.065

CM2122 0.991 0 0 0.009

CM2131 1 0 0 0

CM2132 1 0 0 0

CM231 0.001 0 0 0.999

CM232 1 0 0 0

CM241 0.002 0 0 0.998

CM251 0 0 1 0

CM252 1 0 0 0

CM253 0 0 0 1

CM31 0 0.993 0 0.007

CM3121 1 0 0 0

CM32 0.001 0 0 0.999

CM33 0 0.991 0 0.009

CM341 0 1 0 0

CM342 1 0 0 0

CM351 0.006 0 0 0.994

CM352 1 0 0 0

CM353 0 1 0 0

CM41 0 1 0 0

CM42 0.001 0.002 0 0.997

CM441 0 0.998 0 0.002

CM51 0 0 0 1

CM52 0 0 0 1

CM53 0 0 0 1

CM541 0 0.991 0 0.009

CM542 0 1 0 0

AI3221 0 0.807 0 0.192

AF171 0.033 0.29 0 0.676

AF172 0.03 0.484 0.001 0.485

AF173 0.181 0.194 0.011 0.613

AF174 0.034 0.377 0.001 0.588

AF175 0 0.989 0 0.011

AF2171 0 0 0 0.999

AF2172 0.011 0 0 0.989

AF2173 0.023 0 0 0.977

AF2174 0 0.012 0 0.988

AF2175 0 0 0 1

AF251 0 0 0 1

AF252 0 1 0 0

Spectra Class

1 Class

2 Class

3 Class

4

AF311 0 1 0 0

AF312 0 1 0 0

AF313 0 0 0 1

AF315 0 0 0 1

AF51 0.936 0 0 0.064

AF52 0.987 0 0 0.013

AF53 0.004 0.009 0 0.987

AM211 0.006 0.244 0 0.75

AM212 0 0 0 1

AM221 0.001 0.002 0 0.998

AM2211 1 0 0 0

AM2212 0 0.526 0 0.474

AM222 0.013 0.027 0 0.959

AM2221 0.935 0 0 0.065

AM2222 0.991 0 0 0.009

AM2223 1 0 0 0

AM223 1 0 0 0

AM2291 0.001 0 0 0.999

AM2292 1 0 0 0

AM2293 0.002 0 0 0.998

AM2311 0 0 1 0

AM2312 1 0 0 0

AM2313 0 0 0 1

AM2314 0 0.993 0 0.007

AM2315 1 0 0 0

AM291 0.001 0 0 0.999

AM292 0 0.991 0 0.009

AM293 0 1 0 0

AM311 1 0 0 0

AM312 0.006 0 0 0.994

AM313 1 0 0 0

AM314 0 1 0 0

AM3222 0 1 0 0

AM3223 0.001 0.002 0 0.997

AM3291 0 0.998 0 0.002

AM3292 0 0 0 1

AM3293 0 0 0 1

309

Appendix IV–Fuzzy Clustering (p = 1.2) 3 Cluster 1690-1200 cm-1

Spectra Class

1 Class

2 Class

3

AAF121 1 0 0

AAF122 0 0 1

AAF123 0 0.035 0.965

AAF171 0.667 0 0.333

AAF172 0.028 0 0.972

AAF173 0.998 0 0.002

AAF2121 0.995 0 0.005

AAF2171 0.977 0 0.023

AAF2172 0.01 0.003 0.987

AAF3121 1 0 0

ACF11 1 0 0

ACF111 0.977 0 0.023

ACF12 0.996 0 0.004

ACF21 0 0 1

ACF211 0 0.999 0.001

ACF2111 1 0 0

ACF212 0 0.998 0.002

ACF213 1 0 0

ACF22 0.365 0 0.635

ACF221 0 0.103 0.897

ACF222 0.005 0 0.995

ACF23 0 0.993 0.007

ACF241 0 1 0

ACF242 0 1 0

ACF261 0 1 0

ACF262 0.028 0 0.972

ACF291 0.011 0 0.989

ACF292 0 0 1

ACF3111 0.041 0 0.959

ACF41 0 0.994 0.006

ACF4111 0.982 0 0.018

ACF42 1 0 0

ACF43 0 1 0

ACF5111 0.566 0 0.434

ACF61 0 1 0

ACF62 0 1 0

ACF63 0 0 1

ACF91 1 0 0

ACF92 0 0 1

ACF93 0 0.023 0.977

ACM101 0.045 0 0.955

ACM102 0 0.012 0.987

ACM131 0.086 0 0.914

ACM132 0.136 0 0.864

ACM133 0.999 0 0.001

ACM191 0 0 1

ACM192 0.997 0 0.003

ACM201 0 0.98 0.02

ACM202 0 0.001 0.999

ACM203 0 0 1

ACM2101 0.002 0.042 0.956

ACM2131 0.986 0 0.014

ACM2132 0.001 0 0.999

ACM2191 1 0 0

Spectra Class

1 Class

2 Class

3

ACM2201 0 0 1

ACM2202 0 0.425 0.575

ACM3101 0.999 0 0.001

ACM3191 1 0 0

ACM4101 0.001 0.166 0.832

AF111 0 0.653 0.346

AF112 0 0.001 0.999

AF113 0 0 1

AF2111 0.017 0 0.983

AF2112 0 0 1

AF2203 0.041 0.761 0.198

AF2281 0 0.001 0.999

AF2282 1 0 0

AF2283 0 0 1

AF2284 0 1 0

AF241 0 1 0

AF242 0 0.999 0.001

AF243 0 1 0

AF244 0 1 0

AF245 0 0.997 0.003

AF281 0 0.002 0.998

AF282 0 0 1

AF283 0 0.142 0.858

AF284 0 0.004 0.996

AF3281 1 0 0

AF3282 1 0 0

AF3283 1 0 0

A3221 1 0 0

AF171 0 0 1

AF172 0 0.448 0.552

AF173 0 0.003 0.997

AF174 0 0 1

AF175 0 0 1

AF2171 0 0 1

AF2172 0 0.161 0.839

AF2173 0 0 1

AF2174 1 0 0

AF2175 0.004 0 0.996

AF251 0 0.027 0.973

AF252 0 1 0

AF311 0 0.999 0.001

AF312 0 1 0

AF313 0.032 0 0.968

AF315 0 1 0

AF51 0 1 0

AF52 0 0 1

AF53 0 0 1

AM211 1 0 0

AM212 1 0 0

AM221 1 0 0

AM2211 1 0 0

AM2212 1 0 0

AM222 1 0 0

AM2221 1 0 0

310

Appendix IV - Continued

Spectra Class

1 Class

2 Class

3

AM2222 1 0 0

AM2223 0.999 0 0.001

AM223 1 0 0

AM2291 1 0 0

AM2292 1 0 0

AM2293 0.289 0 0.711

AM2311 0 0.822 0.178

AM2312 0 0.996 0.004

AM2313 0 0.999 0.001

AM2314 0 0 1

AM2315 0 1 0

AM291 0.991 0 0.009

AM292 0.996 0 0.003

AM293 0.999 0 0.001

AM311 0 0.01 0.989

AM312 0.897 0 0.103

AM313 0 0 1

AM314 0.037 0 0.963

AM3222 1 0 0

AM3223 1 0 0

AM3291 0 0 1

AM3292 0 0.155 0.845

AM3293 1 0 0

AM151 0 0.982 0.018

AM152 0 0.994 0.006

AM153 0 0 1

AM201 1 0 0

AM2201 1 0 0

AM2251 0.937 0 0.063

AM2252 0.001 0.718 0.281

AM2253 0.001 0 0.999

AM2261 0.002 0 0.998

AM2262 1 0 0

AM2263 0.999 0 0.001

AM2271 0 0 1

AM2272 0 0 1

AM2273 0 0 1

AM2281 0.15 0 0.85

AM2282 0.139 0 0.861

AM2283 0.811 0 0.189

AM2284 0.987 0 0.013

AM251 0 0.015 0.985

AM252 0 0.004 0.996

AM253 0.001 0 0.999

AM261 0 0 1

AM262 0.97 0 0.03

AM263 0.999 0 0.001

AM271 1 0 0

AM281 0 0 1

AM282 0 0 1

AM283 0 0.053 0.947

AM284 0.001 0 0.999

AM285 0.998 0 0.002

Spectra Class

1 Class

2 Class

3

AM3201 1 0 0

AM3202 0.983 0 0.017

AM3251 0 0.998 0.002

AM3252 0.01 0 0.99

AM3253 0.001 0.002 0.997

AM3261 1 0 0

AM3262 0.984 0 0.016

AM3263 0.965 0 0.035

AM3271 0 0 1

AM3272 1 0 0

AM3273 0.001 0 0.999

AM4201 0 0 1

AM4251 0 0 1

AM4252 0 0 1

AM4253 0.055 0 0.945

AM4271 0 0 1

AM4272 0 0.193 0.807

AM4273 0 0.998 0.002

CF161 0 0.988 0.012

CF162 0.004 0.935 0.061

CF211 0 0 1

CF212 0 0 1

CF213 0 0.119 0.881

CF2161 0.011 0.731 0.258

CF2162 0.002 0.971 0.028

CF2163 0 0.982 0.017

CF2211 1 0 0

CF2212 0 0 1

CF11 0 1 0

CF12 0 0.809 0.191

CF13 0 0.999 0.001

CF14 0 0.999 0.001

CF15 0 0.84 0.16

CF2291 0 0 1

CF2292 0 0 1

CF2293 0 0.002 0.998

CF2294 0 0.432 0.568

CF2295 0 0 1

CF2301 0 0.001 0.999

CF2302 1 0 0

CF2303 0 0.999 0.001

CF2304 0 0.067 0.933

CF2305 0 0.011 0.989

CF2311 0.971 0 0.029

CF2312 0 0.223 0.777

CF2313 1 0 0

CF2314 0 0 1

CF2315 0.059 0 0.941

CF2321 1 0 0

CF2322 0.022 0 0.978

CF241 0.031 0 0.969

CF242 1 0 0

CF243 0.001 0.034 0.965

CF244 0.002 0.002 0.996

311

Appendix IV - Continued

Spectra Class

1 Class

2 Class

3

CF245 0.002 0.003 0.996

CF16 0 1 0

CF17 0 1 0

CF18 0 1 0

CF19 0 1 0

CF110 0 1 0

CF291 0 0 1

CF292 0.083 0 0.917

CF293 0 0.084 0.915

CF294 1 0 0

CF295 0.01 0 0.99

CF301 0.779 0 0.221

CF302 0 0.019 0.98

CF303 0.724 0 0.276

CF304 0 0 1

CF305 0.339 0 0.661

CF311 0 1 0

CF312 0 1 0

CF313 0 1 0

CF314 0 0 1

CF315 0 0 1

CF321 1 0 0

CF322 1 0 0

CF3321 0.991 0 0.009

CF3322 1 0 0

CF41 0.988 0 0.012

CF42 0.305 0 0.695

CF43 0.011 0.001 0.988

CF44 0.275 0 0.725

CF45 0.999 0 0.001

CF101 1 0 0

CF102 0 0 1

CF103 0.002 0 0.998

CF104 1 0 0

CF105 0.998 0 0.002

CF106 0.997 0 0.003

CF107 1 0 0

CF108 1 0 0

CF109 1 0 0

CF1010 0.004 0 0.996

CF1011 0.163 0 0.837

CFA251 0 0 1

CFA252 0.002 0 0.998

CFA253 0.208 0 0.792

CFA254 0.376 0.001 0.623

CM121 0.013 0 0.987

CM122 1 0 0

CM131 0 0 1

CM132 1 0 0

CM133 1 0 0

CM2121 0 0 1

CM2122 0 0 1

CM2131 0 0 1

CM2132 0 0 1

Spectra Class

1 Class

2 Class

3

CM231 1 0 0

CM232 0 0 1

CM241 0 0 1

CM251 0 0.999 0.001

CM252 0 0 1

CM253 0.999 0 0.001

CM31 1 0 0

CM3121 0 0 1

CM32 0.067 0 0.933

CM33 1 0 0

CM341 1 0 0

CM342 0 0.001 0.999

CM351 0.035 0 0.965

CM352 0 0 1

CM353 1 0 0

CM41 1 0 0

CM42 0.991 0 0.009

CM441 1 0 0

CM51 0.014 0 0.986

CM52 0.541 0 0.459

CM53 0.961 0 0.039

CM541 1 0 0

CM542 1 0 0

312

Appendix V–Fuzzy Clustering (p = 1.2) 3-cluster Model 1690-1500 cm-1

Spectra Class

1 Class

2 Class

3

AAF121 1 0 0

AAF122 0.014 0 0.986

AAF123 0 0.012 0.988

AAF171 0.978 0 0.021

AAF172 0.614 0.002 0.384

AAF173 0.999 0 0.001

AAF2121 0.998 0 0.002

AAF2171 0.986 0 0.014

AAF2172 0.152 0.042 0.806

AAF3121 1 0 0

ACF11 1 0 0

ACF111 0.988 0 0.012

ACF12 0.999 0 0.001

ACF21 0 0 1

ACF211 0 1 0

ACF2111 0.999 0 0.001

ACF212 0 0.998 0.002

ACF213 1 0 0

ACF22 0.941 0 0.059

ACF221 0 0.082 0.918

ACF222 0.094 0 0.906

ACF23 0 0.946 0.054

ACF241 0 1 0

ACF242 0 1 0

ACF261 0 1 0

ACF262 0.674 0 0.326

ACF291 0.565 0 0.435

ACF292 0.04 0 0.96

ACF3111 0.054 0 0.946

ACF41 0 0.999 0.001

ACF4111 0.964 0 0.036

ACF42 1 0 0

ACF43 0 1 0

ACF5111 0.839 0 0.161

ACF61 0 1 0

ACF62 0 1 0

ACF63 0.025 0 0.975

ACF91 1 0 0

ACF92 0 0 1

ACF93 0 0.036 0.964

ACM101 0.216 0.004 0.78

ACM102 0.002 0.252 0.746

ACM131 0.012 0 0.988

ACM132 0.006 0 0.994

ACM133 0.701 0 0.299

ACM191 0 0 1

ACM192 0.907 0 0.093

ACM201 0 0.997 0.003

ACM202 0 0.029 0.971

ACM203 0 0 1

ACM2101 0.024 0.231 0.745

Spectra Class

1 Class

2 Class

3

ACM2131 0.052 0 0.948

ACM2132 0 0 1

ACM2191 1 0 0

ACM2201 0 0 1

ACM2202 0 0.913 0.087

ACM3101 0.999 0 0.001

ACM3191 1 0 0

ACM4101 0.001 0.752 0.247

AF111 0 0.627 0.373

AF112 0 0.004 0.996

AF113 0 0 1

AF2111 0.001 0 0.999

AF2112 0 0 1

AF2203 0.077 0.76 0.163

AF2281 0 0 1

AF2282 1 0 0

AF2283 0.004 0 0.996

AF2284 0 0.996 0.004

AF241 0 1 0

AF242 0 1 0

AF243 0 1 0

AF244 0 1 0

AF245 0 0.998 0.002

AF281 0 0 1

AF282 0.006 0 0.994

AF283 0 0 1

AF284 0 0 1

AF3281 1 0 0

AF3282 1 0 0

AF3283 1 0 0

A3221 1 0 0

AF171 0 0 1

AF172 0 0.315 0.685

AF173 0 0.002 0.998

AF174 0 0 1

AF175 0 0 1

AF2171 0 0 1

AF2172 0.001 0.06 0.939

AF2173 0 0 1

AF2174 1 0 0

AF2175 0.008 0 0.992

AF251 0.005 0.326 0.669

AF252 0 1 0

AF311 0 0.998 0.002

AF312 0 1 0

AF313 0.248 0 0.752

AF315 0 1 0

AF51 0 1 0

AF52 0.021 0.001 0.979

AF53 0.003 0 0.996

AM211 1 0 0

313

Appendix V - Continued

Spectra Class

1 Class

2 Class

3

AM262 0.09 0 0.91

AM263 0.626 0 0.374

AM271 0.994 0 0.006

AM281 0 0 1

AM282 0 0 1

AM283 0 0.561 0.439

AM284 0 0 1

AM285 0.97 0 0.03

AM3201 1 0 0

AM3202 1 0 0

AM3251 0 0.996 0.004

AM3252 0 0 1

AM3253 0.001 0.025 0.975

AM3261 1 0 0

AM3262 0.06 0 0.94

AM3263 0.043 0 0.957

AM3271 0 0 1

AM3272 1 0 0

AM3273 0 0 1

AM4201 0.003 0 0.997

AM4251 0 0 1

AM4252 0 0 1

AM4253 0 0 1

AM4271 0 0 1

AM4272 0 0.657 0.342

AM4273 0 0.978 0.022

CF161 0.005 0.961 0.033

CF162 0.027 0.887 0.086

CF211 0 0 1

CF212 0 0 1

CF213 0 0.006 0.994

CF2161 0.021 0.85 0.129

CF2162 0.004 0.965 0.031

CF2163 0 0.991 0.009

CF2211 1 0 0

CF2212 0 0 1

CF16 0 1 0

CF17 0 0.835 0.165

CF18 0.001 0.934 0.065

CF19 0 0.942 0.058

CF110 0.011 0.298 0.691

CF2291 0 0 1

CF2292 0 0 1

CF2293 0 0.007 0.993

CF2294 0 0.16 0.839

CF2295 0 0.004 0.996

CF2301 0 0.002 0.998

CF2302 1 0 0

CF2303 0 0.993 0.007

CF2304 0 0.145 0.855

CF2305 0 0.026 0.974

Spectra Class

1 Class

2 Class

3

AM212 1 0 0

AM221 0.999 0 0.001

AM2211 1 0 0

AM2212 1 0 0

AM222 1 0 0

AM2221 1 0 0

AM2222 1 0 0

AM2223 0.999 0 0.001

AM223 1 0 0

AM2291 0.947 0 0.053

AM2292 1 0 0

AM2293 0.001 0 0.999

AM2311 0 0.965 0.035

AM2312 0 0.994 0.006

AM2313 0 0.995 0.005

AM2314 0 0.006 0.994

AM2315 0 0.998 0.002

AM291 0.993 0 0.007

AM292 0.998 0 0.002

AM293 0.999 0 0.001

AM311 0.001 0.045 0.954

AM312 0.831 0 0.169

AM313 0 0.008 0.992

AM314 0.021 0 0.979

AM3222 1 0 0

AM3223 1 0 0

AM3291 0 0 1

AM3292 0 0.5 0.5

AM3293 1 0 0

AM151 0 0.859 0.14

AM152 0 0.935 0.065

AM153 0 0 1

AM201 1 0 0

AM2201 1 0 0

AM2251 0.014 0 0.986

AM2252 0 0.947 0.052

AM2253 0 0 1

AM2261 0 0 1

AM2262 0.954 0 0.046

AM2263 0.286 0 0.714

AM2271 0 0 1

AM2272 0 0 1

AM2273 0 0 1

AM2281 0.001 0 0.999

AM2282 0 0 1

AM2283 0.009 0 0.991

AM2284 0.112 0 0.888

AM251 0 0.118 0.882

AM252 0 0.054 0.946

AM253 0 0 1

AM261 0 0 1

314

Appendix V - Continued

Spectra Class

1 Class

2 Class

3

CF2311 0.098 0 0.902

CF2312 0 0.661 0.339

CF2313 1 0 0

CF2314 0 0 1

CF2315 0.001 0 0.999

CF2321 1 0 0

CF2322 0.015 0 0.985

CF241 0.987 0 0.013

CF242 1 0 0

CF243 0 0 1

CF244 0.004 0 0.996

CF245 0.012 0 0.988

CF11 0 0.999 0.001

CF12 0 1 0

CF13 0 1 0

CF14 0 1 0

CF15 0 1 0

CF291 0 0 1

CF292 0 0 1

CF293 0 0.446 0.554

CF294 0.944 0 0.056

CF295 0 0 1

CF301 0.788 0 0.212

CF302 0 0.023 0.977

CF303 0.912 0 0.088

CF304 0 0 1

CF305 0.217 0 0.783

CF311 0 0.999 0.001

CF312 0 0.998 0.002

CF313 0 1 0

CF314 0 0 1

CF315 0 0 1

CF321 1 0 0

CF322 1 0 0

CF3321 0.963 0 0.037

CF3322 1 0 0

CF41 1 0 0

CF42 0.999 0 0.001

CF43 0.459 0 0.541

CF44 1 0 0

CF45 1 0 0

CF101 1 0 0

CF102 0.001 0 0.999

CF103 0.057 0 0.943

CF104 1 0 0

CF105 1 0 0

CF106 0.989 0 0.011

CF107 1 0 0

CF108 1 0 0

CF109 1 0 0

CF1010 0.019 0 0.981

Spectra Class

1 Class

2 Class

3

CF1011 0.047 0 0.953

CFA251 0.002 0 0.998

CFA252 0.353 0 0.646

CFA253 0.986 0 0.014

CFA254 0.986 0 0.014

CM121 0.003 0 0.997

CM122 0.998 0 0.002

CM131 0 0 1

CM132 1 0 0

CM133 1 0 0

CM2121 0.001 0 0.999

CM2122 0.002 0.002 0.997

CM2131 0 0 1

CM2132 0 0 1

CM231 0.976 0 0.024

CM232 0 0 1

CM241 0.001 0 0.999

CM251 0 0.98 0.02

CM252 0 0 1

CM253 0.831 0 0.169

CM31 1 0 0

CM3121 0 0 1

CM32 0.566 0 0.434

CM33 1 0 0

CM341 1 0 0

CM342 0 0 1

CM351 0.004 0 0.996

CM352 0 0 1

CM353 1 0 0

CM41 1 0 0

CM42 0.954 0 0.046

CM441 1 0 0

CM51 0.004 0 0.996

CM52 0.681 0 0.319

CM53 0.833 0 0.167

CM541 1 0 0

CM542 1 0 0

315

Appendix VI – FC (p=1.2) African-type Hair Fibres 1750-800 cm-1

Spectra Class

1 Class

2 Class

3

NF4021 0.036 0 0.964

NF4022 0 0 1

NF4023 0 0 1

NF4024 0.001 0 0.999

NF4025 0 0 1

NF4041 0.022 0 0.978

NF4042 0.266 0 0.733

NF4043 1 0 0

NF4044 0.939 0 0.061

NF4045 0.005 0 0.995

NF411 0.422 0.577 0.001

NF412 0.998 0.002 0.001

NF4121 0.736 0.263 0

NF4122 0.909 0.091 0.001

NF4123 0.001 0.999 0

NF4124 0.283 0 0.717

NF4125 0.872 0 0.128

NF413 0.128 0.872 0

NF4131 0.488 0 0.512

NF4132 0.954 0 0.046

NF4133 0.996 0.001 0.004

NF4134 0.998 0 0.002

NF4135 0.001 0 0.999

NF414 0.999 0.001 0.001

NF415 0.001 0 0.999

NF421 0 0 1

NF422 0.166 0 0.833

NF4221 0.137 0 0.863

NF4222 0.002 0 0.998

NF4223 0.001 0 0.999

NF4224 0.002 0 0.998

NF4225 0 0 1

NF423 0.28 0 0.72

NF424 0.118 0.882 0

NF425 0.114 0 0.886

NF431 0 0 1

NF432 0.998 0 0.002

NF4321 0.999 0 0.001

NF4322 0.999 0.001 0

NF4323 0 0 1

NF4324 0 0 1

NF4325 0.001 0 0.999

NF433 0 0 1

NF434 0.002 0 0.998

NF435 0 0 1

NF441 0.168 0 0.832

NF442 0 0 1

NF4421 0.999 0 0.001

NF4422 0.006 0.994 0

NF4423 0.001 0.999 0

NF4424 0.002 0.997 0

Spectra Class

1 Class

2 Class

3

NF4425 0.091 0.909 0

NF443 0.001 0 0.999

NF4431 0.761 0.239 0

NF4432 1 0 0

NF4433 0.999 0 0.001

NF444 0 0 1

NF4441 0.94 0.001 0.06

NF4442 0.712 0 0.288

NF4443 0.085 0.915 0

NF4444 0.021 0 0.979

NF445 0.993 0 0.007

NF446 0 0 1

NF451 1 0 0

NF452 0.017 0.983 0

NF4521 0.357 0 0.643

NF4522 1 0 0

NF4523 0.996 0 0.004

NF4524 0.994 0 0.006

NF4525 0.547 0 0.453

NF453 1 0 0

NF4531 0.978 0 0.022

NF4532 1 0 0

NF4533 1 0 0

NF4534 1 0 0

NF4535 1 0 0

NF454 1 0 0

NF455 0.01 0.99 0

NF71 0.004 0.996 0

NM181 1 0 0

NM182 0.055 0 0.945

NM183 1 0 0

NM211 0.999 0 0.001

NM212 1 0 0

NM213 0.975 0.025 0

NM2181 0.13 0.87 0

NM2182 0.134 0.866 0

NM221 1 0 0

NM222 1 0 0

NM223 0 1 0

NM2231 0.076 0.924 0

NM2232 0.012 0.987 0

NM2241 1 0 0

NM2301 0 1 0

NM2302 0 1 0

NM2303 0 1 0

NM231 0.013 0.987 0

NM2311 1 0 0

NM2312 0.017 0 0.983

NM2313 0.997 0.003 0

NM2314 0 0 1

NM2315 0.004 0.996 0

316

Appendix VI - Continued

Spectra Class

1 Class

2 Class

3

NM4231 0 1 0

NM4232 0.053 0.946 0

NM4233 0.017 0.983 0

NM4234 0.971 0 0.028

NM4235 0.992 0.008 0

NM4241 0.996 0 0.004

NM431 0 1 0

NM4322 0.91 0.089 0.001

NM442 0.654 0.341 0.004

NM4421 0 0 1

NM4422 0 0 1

NM4423 0 0 1

NM443 0.992 0.008 0

NM4431 0.619 0.381 0

NM4432 0.004 0 0.996

NM4433 0 0 1

NM444 0.986 0 0.014

NM445 0 0 1

NM451 0.999 0 0.001

NM4521 0 0 1

NM4522 0 0 1

NM4523 0 0 1

NM4524 1 0 0

NM4525 0.798 0 0.202

NM4621 0 0 1

NM4622 0.991 0.008 0.002

NM4623 0 0 1

NM4631 0 0 1

NM4632 0.996 0 0.004

NM4633 0.126 0.874 0

NM472 0.002 0 0.998

NM4721 0 0 1

NM4722 1 0 0

NM4723 1 0 0

NM473 0 1 0

NM4731 0.001 0 0.999

NM4732 0.976 0.024 0

NM4733 0.096 0 0.904

NM4741 0.992 0 0.008

NM4742 0.995 0.005 0

NM4743 0 0 1

NM4821 0.985 0.015 0

NM4822 1 0 0

NM4823 1 0 0

NM483 0.001 0 0.999

NM4831 1 0 0

NM4832 0.305 0 0.695

NM4833 0 1 0

NM4841 1 0 0

NM4842 1 0 0

NM4843 0.96 0 0.04

NM581 0.763 0.225 0.012

Spectra Class

1 Class

2 Class

3

NM232 0.876 0.124 0

NM241 0.002 0.998 0

NM242 0.001 0.999 0

NM243 0 1 0

NM244 0 1 0

NM281 0.06 0.937 0.002

NM301 0 1 0

NM302 0.001 0.999 0

NM303 0 1 0

NM304 0.002 0.998 0

NM311 0.001 0.999 0

NM312 0.002 0.998 0

NM313 0 1 0

NM314 0.049 0.951 0

NM315 0.003 0.997 0

NM321 0 1 0

NM322 0 1 0

NM323 0 1 0

NM3231 0.013 0.987 0

NM3232 0.009 0.991 0

NM3241 1 0 0

NM3242 0.056 0.944 0

NM3243 0 1 0

NM3244 1 0 0

NM3301 0 1 0

NM3302 0.016 0.984 0

NM3303 0.972 0.028 0

NM331 1 0 0

NM401 0.004 0 0.996

NM402 0.001 0 0.999

NM4021 0 0 1

NM4022 0.001 0.999 0

NM403 0 0 1

NM4031 0.996 0 0.003

NM4032 0 0 1

NM4033 1 0 0

NM411 0.998 0.002 0

NM412 0.981 0.019 0

NM4121 0.009 0.991 0

NM4122 0.28 0 0.72

NM4123 0 0 1

NM413 0.166 0 0.834

NM4131 0 0 1

NM4132 0 0 1

NM4133 0 0 1

NM421 0 0 1

NM422 0 0 1

NM4221 0.999 0 0.001

NM4222 1 0 0

NM4223 0 0 1

NM423 0.004 0 0.996

317

Appendix VII – FC (p = 1.2) African-type Hair Fibres 1690-1500 cm-1

Spectra Class

1 Class

2 Class

3

NF271 0.999 0 0.001

NF272 0.989 0 0.011

NF273 0.021 0 0.979

NF274 0.001 0.075 0.924

NF341 0 0 1

NF371 0 1 0

NF372 0 1 0

NF373 0 1 0

NF374 0 0.999 0.001

NF4021 0.979 0 0.021

NF4022 1 0 0

NF4023 1 0 0

NF4024 1 0 0

NF4025 1 0 0

NF4041 1 0 0

NF4042 1 0 0

NF4043 0.166 0 0.834

NF4044 0.91 0 0.09

NF4045 0.998 0 0.002

NF411 0.111 0.016 0.874

NF412 0.293 0.003 0.704

NF4121 0.017 0.004 0.979

NF4122 0.019 0.002 0.979

NF4123 0.004 0.203 0.793

NF4124 1 0 0

NF4125 0.984 0 0.016

NF413 0.029 0.02 0.951

NF4131 1 0 0

NF4132 0.995 0 0.005

NF4133 0.989 0 0.011

NF4134 0.911 0 0.089

NF4135 1 0 0

NF414 0.63 0.001 0.369

NF415 0.999 0 0.001

NF421 1 0 0

NF422 0.993 0 0.007

NF4221 0.981 0 0.019

NF4222 1 0 0

NF4223 1 0 0

NF4224 1 0 0

NF4225 1 0 0

NF423 0.982 0 0.018

NF424 0.001 0.027 0.973

NF425 0.997 0 0.003

NF431 1 0 0

NF432 0.134 0 0.866

NF4321 0.084 0 0.916

NF4322 0 0 1

NF4323 1 0 0

NF4324 1 0 0

NF4325 1 0 0

Spectra Class

1 Class

2 Class

3

NF433 1 0 0

NF434 1 0 0

NF435 1 0 0

NF441 0.903 0 0.097

NF442 1 0 0

NF4421 0 0 1

NF4422 0 0.994 0.006

NF4423 0 0.992 0.008

NF4424 0 0.998 0.002

NF4425 0 0.836 0.164

NF443 1 0 0

NF4431 0.001 0.082 0.916

NF4432 0 0 1

NF4433 0.001 0 0.999

NF444 0.993 0 0.007

NF4441 0 0 1

NF4442 0.001 0 0.999

NF4443 0 0.999 0.001

NF4444 0.252 0 0.748

NF445 0.01 0.008 0.982

NF446 0.999 0 0.001

NF451 0 0 1

NF452 0 0.954 0.045

NF4521 0.894 0 0.106

NF4522 0.002 0 0.998

NF4523 0.301 0 0.699

NF4524 0.387 0 0.613

NF4525 0.446 0 0.554

NF453 0 0 1

NF4531 0.01 0 0.99

NF4532 0 0 1

NF4533 0 0 1

NF4534 0.057 0 0.943

NF4535 0 0 1

NF454 0 0 1

NF455 0 0.998 0.002

NF71 0 0.988 0.012

NM181 0 0 1

NM182 1 0 0

NM183 0.001 0 0.999

NM211 0.122 0 0.878

NM212 0 0 1

NM213 0 0 1

NM2181 0 0 1

NM2182 0 0 1

NM221 0 0 1

NM222 0 0 1

NM223 0 0.273 0.727

NM2231 0 0 1

NM2232 0 0 1

NM2241 0.025 0 0.975

318

Appendix VII - Continued

Spectra Class

1 Class

2 Class

3

NM2301 0 1 0

NM2302 0 0.999 0.001

NM2303 0 1 0

NM231 0 0 1

NM2311 0.146 0 0.854

NM2312 1 0 0

NM2313 0 0 1

NM2314 1 0 0

NM2315 0.003 0.264 0.733

NM232 0.002 0 0.998

NM241 0 0.001 0.999

NM242 0 1 0

NM243 0 0.006 0.994

NM244 0 0.004 0.996

NM281 0 0.921 0.079

NM301 0 0.993 0.007

NM302 0 1 0

NM303 0 1 0

NM304 0 1 0

NM311 0 0 1

NM312 0 0 1

NM313 0 0.005 0.995

NM314 0 0 1

NM315 0 0 1

NM321 0 0 1

NM322 0 0.301 0.699

NM323 0 0.324 0.676

NM3231 0.02 0.016 0.964

NM3232 0 0.992 0.008

NM3241 0.111 0 0.889

NM3242 0 0 0.999

NM3243 0 0.216 0.784

NM3244 0.001 0 0.999

NM3301 0 0.91 0.09

NM3302 0 0.006 0.994

NM3303 0 0 1

NM331 0 0 1

NM401 1 0 0

NM402 1 0 0

NM4021 1 0 0

NM4022 0 0.995 0.005

NM403 1 0 0

NM4031 0.243 0 0.757

NM4032 1 0 0

NM4033 0 0 1

NM411 0 0 0.999

NM412 0 0.001 0.998

NM4121 0.001 0.038 0.962

NM4122 0.943 0 0.057

NM4123 1 0 0

NM413 0.319 0 0.681

Spectra Class

1 Class

2 Class

3

NM4131 1 0 0

NM4132 1 0 0

NM4133 1 0 0

NM421 1 0 0

NM422 1 0 0

NM4221 0.175 0 0.825

NM4222 0.003 0 0.997

NM4223 1 0 0

NM423 0.995 0 0.005

NM4231 0 0.13 0.87

NM4232 0 0 1

NM4233 0 0 1

NM4234 0.905 0 0.095

NM4235 0 0 1

NM4241 0.961 0 0.039

NM431 0.004 0.181 0.815

NM4322 0.002 0 0.997

NM442 0.044 0.089 0.867

NM4421 1 0 0

NM4422 1 0 0

NM4423 1 0 0

NM443 0.009 0.013 0.979

NM4431 0 0 1

NM4432 1 0 0

NM4433 1 0 0

NM444 0.559 0 0.441

NM445 1 0 0

NM451 0 0 1

NM4521 1 0 0

NM4522 0.999 0 0.001

NM4523 1 0 0

NM4524 0 0 1

NM4525 0.011 0 0.989

NM4621 1 0 0

NM4622 0.002 0.004 0.995

NM4623 1 0 0

NM4631 1 0 0

NM4632 0.226 0 0.774

NM4633 0.001 0 0.999

NM472 0.999 0 0.001

NM4721 1 0 0

NM4722 0 0 1

NM4723 0 0 1

NM473 0 0.067 0.932

NM4731 1 0 0

NM4732 0 0 1

NM4733 0.898 0 0.102

NM4741 0.009 0 0.991

NM4742 0 0 0.999

NM4743 1 0 0

NM482 0 1 0

319

Appendix VII - Continued

Spectra Class

1 Class

2 Class

3

NM4821 0 0 1

NM4822 0 0 1

NM4823 0 0 1

NM483 1 0 0

NM4831 0 0 1

NM4832 0.428 0 0.572

NM4833 0 0.903 0.097

NM4841 0 0 1

NM4842 0.018 0 0.982

NM4843 0.018 0 0.982

NM581 0 0 1

320

Appendix VIII – FC (p = 1.2) Mildly Treated Database 1690-1500 cm-1

Spectra Class

1 Class

2 Class

3 Class

4

ACF291 0.42 0.025 0 0.554

ACF292 0.948 0.014 0 0.038

ACF91 0 0 0 1

ACF92 1 0 0 0

ACF93 0.008 0 0.992 0

ACM101 0.188 0.712 0.008 0.092

ACM102 0.006 0.031 0.962 0

ACM131 0.01 0.984 0 0.005

ACM132 0.003 0.996 0 0.002

ACM133 0.01 0.434 0 0.556

ACM201 0 0 1 0

ACM202 0 0 1 0

ACM203 0.901 0.094 0.005 0

ACM2101 0.121 0.453 0.416 0.01

ACM2131 0.012 0.322 0 0.666

ACM2132 1 0 0 0

ACM2201 0 1 0 0

ACM2202 0 0 1 0

AF111 0 0 1 0

AF112 0.008 0 0.992 0

AF113 1 0 0 0

AF2111 1 0 0 0

AF2112 0.998 0.002 0 0

AF171 0.031 0.969 0 0

AF172 0 0 1 0

AF173 0.033 0.007 0.96 0

AF174 0.575 0.419 0.006 0

AF175 0 1 0 0

AF2171 0.924 0.076 0 0

AF2172 0.113 0.003 0.883 0

AF2173 0.837 0.162 0 0.001

AF2174 0 0.001 0 0.998

AF2175 0.01 0.974 0 0.016

AM2251 0.001 0.946 0 0.053

AM2252 0.001 0.001 0.999 0

AM2253 0 1 0 0

AM2261 0 1 0 0

AM2262 0 0 0 0.999

AM2263 0.001 0.05 0 0.95

AM2271 0.536 0.453 0.01 0

AM2272 0.004 0.996 0 0

AM2273 0.02 0.979 0 0.001

AM2281 0.001 0.998 0 0

AM2282 0.072 0.92 0 0.008

AM2283 0.004 0.899 0 0.098

AM2284 0.003 0.093 0 0.904

AM251 0 0 1 0

AM252 0 0 0.999 0

AM253 0 1 0 0

AM261 0.844 0.156 0 0

Spectra Class

1 Class

2 Class

3 Class

4

AM262 0.006 0.618 0 0.377

AM263 0 0.002 0 0.998

AM271 0 0.002 0 0.998

AM281 0.017 0.982 0.001 0

AM282 0.125 0.872 0.003 0

AM283 0 0 1 0

AM284 0.177 0.812 0.01 0

AM285 0 0.002 0 0.998

AM3251 0.008 0.002 0.99 0

AM3252 0 1 0 0

AM3253 0.015 0.135 0.85 0

AM3261 0.001 0.004 0 0.995

AM3262 0.003 0.544 0 0.453

AM3263 0.003 0.686 0 0.311

AM3271 0.003 0.997 0 0

AM3272 0.001 0.002 0 0.998

AM3273 0.002 0.998 0 0

AM4201 0.991 0.007 0 0.002

AM4251 0.002 0.998 0 0

AM4252 0 1 0 0

AM4253 0.002 0.996 0 0.002

AM4271 0.059 0.941 0 0

AM4272 0 0 1 0

AM4273 0.004 0.002 0.994 0

CF211 0.982 0.011 0.007 0

CF212 0.999 0.001 0 0

CF213 0.715 0.005 0.279 0

CF2211 0 0 0 1

CF2212 0.904 0.096 0 0

CF2291 0 1 0 0

CF2292 0 1 0 0

CF2293 0.005 0.015 0.98 0

CF2294 0 0.001 0.999 0

CF2295 0.032 0.8 0.168 0

CF2301 0.527 0.008 0.465 0

CF2302 0.001 0.002 0 0.997

CF2303 0.001 0 0.999 0

CF2304 0.011 0 0.988 0

CF2305 0.053 0.001 0.946 0

CF291 0 1 0 0

CF292 0 0.999 0 0.001

CF293 0 0 0.999 0

CF294 0 0 0 1

CF295 0 1 0 0

CF301 0.001 0 0 0.998

CF302 0.037 0.002 0.961 0

CF303 0 0 0 1

CF304 1 0 0 0

CF305 0.015 0.002 0 0.983

CM121 0.003 0.981 0 0.016

321

Appendix VIII - Continued

Spectra Class

1 Class

2 Class

3 Class

4

CM122 0 0 0 1

CM2121 0.19 0.799 0.006 0.005

CM2122 0.1 0.856 0.039 0.005

CM3121 0.974 0.026 0 0

NF451 0 1 0 0

NF452 0.01 0.081 0.908 0

NF4521 0 0.001 0 0.998

NF4522 0.11 0.758 0 0.132

NF4523 0.001 0.003 0 0.996

NF4524 0.007 0.029 0 0.965

NF4525 0 0.001 0 0.999

NF453 0.003 0.997 0 0

NF4531 0 0 0 1

NF4532 0.019 0.981 0 0

NF4533 0.009 0.991 0 0

NF4534 0.007 0.013 0 0.979

NF4535 0.242 0.424 0 0.335

NF454 0.002 0.998 0 0

NF455 0.002 0.003 0.995 0

NM181 0.859 0.133 0 0.008

NM182 0 0 0 1

NM183 0.039 0.01 0 0.951

NM211 0 0 0 1

NM212 0.996 0.004 0 0.001

NM213 1 0 0 0

NM2181 1 0 0 0

NM2182 1 0 0 0

NM221 0.999 0.001 0 0

NM222 0.193 0.024 0 0.783

NM223 0.304 0.003 0.694 0

NM2231 0.997 0.003 0 0

NM2232 0.999 0.001 0 0

NM2241 0.016 0.002 0 0.982

NM231 0.993 0.007 0 0

NM2311 0.019 0.022 0 0.959

NM2312 0 0 0 1

Spectra Class

1 Class

2 Class

3 Class

4

NM2313 0.99 0.01 0 0

NM2314 0.004 0.005 0 0.99

NM2315 0.63 0.052 0.316 0.002

NM232 0.948 0.036 0 0.017

NM241 0.999 0.001 0 0

NM242 0 0 1 0

NM243 0.987 0.006 0.007 0

NM244 0.986 0.007 0.006 0

NM311 1 0 0 0

NM312 1 0 0 0

NM313 0.998 0.001 0.001 0

NM314 0.996 0.002 0 0.002

NM315 1 0 0 0

NM321 1 0 0 0

NM322 0.36 0.002 0.637 0

NM323 0.074 0.003 0.923 0

NM3231 0.902 0.055 0.014 0.029

NM3232 0.017 0.004 0.979 0

NM3241 0.006 0.003 0 0.991

NM3242 0.955 0.039 0.001 0.005

NM3243 0.361 0.023 0.616 0

NM3244 0.456 0.133 0 0.411

NM331 0.983 0.006 0 0.012

NM482 0.002 0.001 0.997 0

NM4821 0.998 0.002 0 0

NM4822 0.063 0.008 0 0.929

NM4823 0.986 0.013 0 0.001

NM483 0 0 0 1

NM4831 0.983 0.016 0 0.001

NM4832 0.001 0 0 0.999

NM4833 0.015 0.003 0.982 0

NM4841 0.744 0.048 0 0.208

NM4842 0.001 0 0 0.998

NM4843 0.003 0.001 0 0.996

NM581 0.03 0.97 0 0

322

Appendix IX – FC (p =1.2) Treated Hair Database 1690-1500 cm-1

Spectra Class 1 Class 2 Class 3

AAF171 0.135 0.862 0.002

AAF172 0.011 0.989 0

AAF173 0.756 0.173 0.071

AAF2171 0.332 0.638 0.03

AAF2172 0.005 0.995 0

ACF111 0.088 0.912 0

ACF2111 0.743 0.149 0.109

ACF4111 0.196 0.793 0.011

ACF5111 0.003 0.997 0

ACM191 0.004 0.996 0

ACM192 0.727 0.156 0.117

ACM2191 0.081 0 0.919

ACM3191 0 0 1

A3221 0.983 0 0.017

AM211 1 0 0

AM212 0.208 0 0.792

AM221 1 0 0

AM2211 0.999 0 0.001

AM2212 0 0 1

AM222 0 0 1

AM2221 0.058 0 0.942

AM2222 1 0 0

AM2223 0 0 1

AM223 0.939 0 0.061

AM2291 0.983 0.016 0

AM2292 0.733 0 0.267

AM2293 0.001 0.999 0

AM291 0.802 0.198 0

AM292 0.002 0 0.998

AM293 0 0 1

AM3222 1 0 0

AM3223 1 0 0

AM201 0 0 1

AM2201 0.012 0 0.988

AM3201 0 0 1

AM3202 0.999 0 0

CF2321 0.997 0 0.003

CF2322 0.007 0.993 0

CF241 0.817 0.183 0

CF242 0.04 0 0.96


CF243 0 1 0

CF244 0 1 0

CF245 0 1 0

CF321 1 0 0

CF322 1 0 0

CF3321 0.94 0.06 0

CF3322 1 0 0

CF41 1 0 0

CF42 0.906 0.094 0

CF43 0.011 0.989 0

CF44 0.992 0.008 0

CF45 0 0 1

CF81 1 0 0

CF821 0 1 0

CF822 0 1 0

CF823 1 0 0

CF824 0.898 0.102 0

CF825 0.901 0.099 0

CF831 0 0 1

CF832 0 0 1

CF833 0.017 0 0.983

CF834 0.006 0.994 0

CF835 0.066 0.934 0

CM231 1 0 0

CM232 0.008 0.992 0

CM31 0.978 0 0.022

CM32 0.943 0.057 0.001

CM33 1 0 0

CM341 0 0 1

CM342 0.002 0.998 0

CM41 0 0 1

CM42 0.86 0.14 0

CM441 0.999 0 0.001

CM541 1 0 0

CM542 0 0 1

NF4021 0.558 0.01 0.431

NF4022 0.014 0 0.986

NF4023 0.999 0 0.001

NF4024 0.999 0 0.001

NF4025 0.952 0 0.048

323

Appendix IX - Continued


NF4041 0.977 0 0.023

NF4042 1 0 0

NF4043 0.898 0.102 0

NF4044 0.998 0.002 0

NF4045 1 0 0

NF421 0.301 0 0.699

NF422 0.999 0.001 0

NF4221 0.993 0.004 0.003

NF4222 0.935 0.001 0.065

NF4223 0.887 0 0.113

NF4224 0.988 0 0.012

NF4225 0.007 0 0.993

NF423 1 0 0

NF424 0.001 0.999 0

NF425 0.998 0.001 0.001

NF431 0.998 0 0.002

NF432 0.794 0.206 0

NF4321 0.84 0.159 0

NF4322 0 1 0

NF4323 0.002 0 0.998

NF4324 1 0 0

NF4325 1 0 0

NF433 0.015 0 0.985

NF434 1 0 0

NF435 0.336 0 0.664

NM401 1 0 0

NM402 0.003 0 0.997

NM4021 0.999 0 0.001

NM4022 0.032 0.966 0.002

NM403 0.114 0 0.886

NM4031 0.898 0.101 0.001

NM4032 0.873 0 0.127

NM4033 0.039 0.961 0

NM442 0.006 0.994 0

NM4421 0 0 1

NM4422 0.029 0 0.971

NM4423 0.98 0 0.02

NM443 0 1 0

NM4431 0 1 0

NM4432 1 0 0

NM4433 0.995 0 0.005

NM444 0.622 0.377 0.001

NM445 0 0 1

324

Appendix X – Alternative Spectral Regions for the Proposed Forensic

Protocol (Continued from Chapter 4)

4.2.3.1 Chemometric Analysis of Single Human Hair Fibres using Alternative Spectral

Regions - 1690-1360 cm-1


(blue), the chemically treated fibres (pink)and the mildly treated fibres (green) using the


.

-20

-15

-10

-5

0

5

10

-30 -20 -10 0 10 20 30

PC1 (77.8%)

PC

2 (

12

.2%

)


Chemically Treated

Untreated

Mildly Treated

CFUN 1

CFTR 10

Increase in

Physical/Chemical

Treatment

325

Figure 4.20 - PC1 Loadings plot of the chemically treated fibres (positive loadings) and

the untreated and mildly treated fibres (negative loadings) between 1690-1360 cm-1

.

Figure 4.21– PC2 Loadings plot of the mildly treated fibres (positive loadings) and the

untreated and chemically treated fibres (negative loadings) between 1690-1360 cm-1

.

326

Table 4.9 – PROMETHEE II Net Flows of the 1690-1360 cm-1

Database


1 UN 0.951

2 CFUN18 0.911

3 UN 0.892

4 CFUN19 0.89

5 UN 0.875

6 UN 0.872

7 UN 0.865

8 UN 0.853

9 UN 0.85

10 CFUN110 0.847

11 UN 0.825

12 CFUN17 0.812

13 MTR 0.793

14 CFUN13 0.777

15 CFUN16 0.76

16 UN 0.748

17 UN 0.733

18 UN 0.726

19 UN 0.716

20 CFUN14 0.710

21 CFUN15 0.691

22 MTR 0.643

23 CFUN11 0.633

24 UN 0.632

25 UN 0.631

26 MTR 0.621

27 UN 0.616

28 CFUN12 0.594

29 UN 0.582

30 TR 0.549

31 MTR 0.479

32 UN 0.477

33 MTR 0.422

34 MTR 0.408

35 MTR 0.396

36 UN 0.393

37 UN 0.381

38 TR 0.372

39 MTR 0.342

40 MTR 0.326

41 MTR 0.324

42 UN 0.318

43 UN 0.305

44 UN 0.258

45 MTR 0.238

46 TR 0.235

47 MTR 0.234

48 TR 0.227

49 MTR 0.219

50 MTR 0.186

51 UN 0.177

52 TR 0.176

53 TR 0.171

54 MTR 0.154

55 TR 0.150


56 CFTR102 0.147

57 TR 0.129

58 MTR 0.110

59 CFTR103 0.100

60 MTR 0.080

61 MTR 0.077

62 MTR 0.076

63 MTR 0.072

64 MTR 0.069

65 MTR 0.069

66 UN 0.063

67 MTR 0.061

68 TR 0.059

69 MTR 0.051

70 MTR 0.043

71 MTR 0.039

72 CFTR105 0.035

73 MTR 0.033

74 MTR 0.032

75 MTR 0.029

76 MTR 0.019

77 MTR 0.01

78 MTR 0.008

79 MTR -0.001

80 MTR -0.006

81 MTR -0.009

82 MTR -0.012

83 MTR -0.014

84 TR -0.042

85 MTR -0.049

86 CFTR1010 -0.049

87 MTR -0.053

88 MTR -0.056

89 MTR -0.057

90 MTR -0.064

91 MTR -0.065

92 TR -0.070

93 MTR -0.072

94 MTR -0.078

95 MTR -0.084

96 CFTR101 -0.088

97 MTR -0.089

98 MTR -0.089

99 MTR -0.097

100 MTR -0.103

101 CFTR104 -0.106

102 TR -0.115

103 MTR -0.117

104 TR -0.117

105 MTR -0.117

106 MTR -0.125

107 TR -0.133

108 MTR -0.136

109 CFTR106 -0.137

110 MTR -0.139

327



111 MTR -0.144

112 MTR -0.145

113 MTR -0.147

114 MTR -0.150

115 MTR -0.164

116 MTR -0.171

117 MTR -0.175

118 MTR -0.177

119 MTR -0.177

120 MTR -0.183

121 CFTR109 -0.190

122 MTR -0.191

123 TR -0.191

124 CFTR1011 -0.200

125 MTR -0.206

126 MTR -0.212

127 TR -0.215

128 MTR -0.216

129 TR -0.217

130 TR -0.221

131 CFTR107 -0.221

132 MTR -0.223

133 MTR -0.224

134 MTR -0.231

135 MTR -0.234

136 MTR -0.236

137 MTR -0.242

138 MTR -0.245

139 TR -0.246

140 TR -0.248

141 TR -0.252

142 MTR -0.255

143 CFTR108 -0.257

144 MTR -0.259

145 TR -0.263

146 TR -0.264

147 TR -0.268

148 MTR -0.269

149 TR -0.27

150 MTR -0.271

151 MTR -0.276

152 MTR -0.279

153 MTR -0.284

154 TR -0.288

155 MTR -0.292

156 MTR -0.292

157 MTR -0.297

158 TR -0.303

159 MTR -0.312

160 TR -0.319

161 MTR -0.319

162 MTR -0.32

163 TR -0.320

164 TR -0.323

165 MTR -0.326


166 MTR -0.33

167 TR -0.336

168 TR -0.348

169 TR -0.352

170 TR -0.358

171 TR -0.362

172 MTR -0.367

173 TR -0.367

174 MTR -0.369

175 MTR -0.372

176 MTR -0.379

177 MTR -0.386

178 TR -0.391

179 TR -0.396

180 MTR -0.398

181 TR -0.399

182 TR -0.405

183 MTR -0.412

184 MTR -0.414

185 TR -0.417

186 TR -0.417

187 TR -0.454

188 TR -0.456

189 TR -0.458

190 TR -0.485

191 MTR -0.495

192 MTR -0.512

193 TR -0.512

194 TR -0.514

195 TR -0.519

196 TR -0.523

197 MTR -0.525

198 TR -0.528

199 TR -0.537

200 TR -0.559

201 TR -0.601

328

Δ 100 %

Figure 4.22 – GAIA analysis of the 201 spectra for the 1690-1360 cm-1

hair fibre




Chemically Treated

Untreated

Mild Treatment

329


Region


(blue), mildly treated fibres (green) and the chemically treated fibres (pink) of second

derivative spectra between 1750-800 cm-1

.

Unfortunately with second derivative spectra, the variables (loadings) that give rise to

the separation of the spectra cannot be the used as second derivative spectra consist of

minima and maxima peaks. Only the minima peaks are used for characterisation of the

spectra. Hence, the PC1 and PC2 loadings plots are complex because it is too difficult

to ascertain whether the loadings correlate to the minima or maxima peaks.

-20

-15

-10

-5

0

5

10

15

20

25

30

35

-30 -20 -10 0 10 20 30 40

PC1 (27.5%)

PC

2 (

15

.5%

)Untreated Chemically Treated Mildly Treated

Mildly Treated

Chemically Treated

UntreatedIncrease in

Physical/Chemical

Treatment

CFUN 1

CFTR 10

330

Table 4.10 - PROMETHEE II Net Flows 2nd

Derivative 1750-1800 cm-1

Database


1 Un 0.965

2 Un 0.933

3 Un 0.862

4 Un 0.783

5 Un 0.777

6 Un 0.747

7 Tr 0.734

8 Un 0.707

9 Un 0.685

10 Un 0.675

11 Un 0.651

12 Un 0.631

13 Un 0.631

14 Un 0.616

15 Tr 0.605

16 Un 0.589

17 Tr 0.571

18 Un 0.555

19 Un 0.54

20 Un 0.529

21 Tr 0.520

22 Tr 0.517

23 Tr 0.512

24 Un 0.511

25 Un 0.510

26 Un 0.509

27 Un 0.507

28 Tr 0.487

29 Un 0.437

30 CF17 0.407

31 Un 0.388

32 Tr 0.382

33 Un 0.379

34 Un 0.372

35 Un 0.372

36 Tr 0.370

37 Tr 0.352

38 Tr 0.349

39 CF11 0.346

40 CF18 0.326

41 Un 0.315

42 CF12 0.304

43 CF14 0.301

44 CF110 0.293

45 Tr 0.287

46 Un 0.269

47 Tr 0.264

48 Un 0.247

49 Un 0.246

50 CF16 0.24


51 Tr 0.238

52 Un 0.223

53 Tr 0.199

54 CF13 0.185

55 Tr 0.180

56 Un 0.176

57 CF19 0.171

58 Un 0.167

59 MT 0.162

60 MT 0.162

61 MT 0.159

62 Tr 0.147

63 MT 0.145

64 Tr 0.141

65 Un 0.129

66 CF15 0.122

67 MT 0.114

68 Tr 0.106

69 Tr 0.103

70 Tr 0.100

71 MT 0.099

72 Tr 0.092

73 Tr 0.091

74 Tr 0.089

75 Tr 0.087

76 Tr 0.086

77 Tr 0.078

78 Tr 0.075

79 Un 0.068

80 MT 0.043

81 Tr 0.041

82 Tr 0.037

83 MT 0.033

84 Tr -0.015

85 Tr -0.02

86 Tr -0.020

87 CF102 -0.024

88 MT -0.026

89 Tr -0.038

90 MT -0.042

91 Tr -0.051

92 Tr -0.051

93 MT -0.052

94 MT -0.052

95 Tr -0.056

96 MT -0.073

97 MT -0.076

98 MT -0.076

99 Tr -0.082

100 MT -0.084

331



101 Tr -0.084

102 Tr -0.091

103 MT -0.091

104 Tr -0.099

105 Tr -0.111

106 Tr -0.115

107 CF103 -0.141

108 Tr -0.160

109 Tr -0.162

110 Tr -0.167

111 Tr -0.169

112 Tr -0.169

113 MT -0.171

114 Tr -0.173

115 CF106 -0.183

116 Tr -0.194

117 Tr -0.197

118 MT -0.218

119 Tr -0.230

120 MT -0.232

121 Tr -0.237

122 MT -0.245

123 Tr -0.252

124 Tr -0.254

125 MT -0.255

126 CF1011 -0.257

127 CF1010 -0.258

128 MT -0.261

129 Tr -0.267

130 Tr -0.270

131 Tr -0.279

132 Tr -0.281

133 CF101 -0.281

134 MT -0.282

135 MT -0.285

136 MT -0.295

137 Tr -0.297

138 MT -0.298


139 Tr -0.301

140 MT -0.301

141 CF105 -0.332

142 Tr -0.334

143 MT -0.335

144 MT -0.346

145 Tr -0.361

146 Tr -0.365

147 Tr -0.394

148 CF104 -0.397

149 Tr -0.402

150 Tr -0.419

151 Tr -0.425

152 Tr -0.45

153 MT -0.455

154 MT -0.464

155 Tr -0.480

156 MT -0.481

157 Tr -0.517

158 MT -0.537

159 Tr -0.556

160 MT -0.559

161 Un -0.572

162 MT -0.576

163 Tr -0.603

164 Un -0.614

165 Tr -0.624

166 Tr -0.637

167 Tr -0.675

168 Tr -0.702

169 Tr -0.703

170 CF109 -0.706

171 Tr -0.707

172 CF107 -0.725

173 CF108 -0.764

174 MT -0.767

175 MT -0.810

176 MT -0.899

332

Δ 100 %

Figure 4.24 - GAIA analysis of the 176 second derivative spectra for the 1750-800 cm-1

hair fibre database; ▲ untreated fibres, ■ chemically treated fibres, ■ mildly treated

hair fibres, ● pi (Π) decision-making axis, and ■ PC1 and PC2 criterion variables


Mild Treatment

Chemically Treated

Untreated

333


Region


(blue), mildly treated fibres (green) and the chemically treated fibres (pink) of second

derivative spectra between 1690-1500 cm-1

.

-10

-5

0

5

10

15

20

25

-15 -10 -5 0 5 10 15 20

PC1 (47.9%)

PC

2 (

21

.9%

)


Chemically

Treated

Mildly Treated

Untreated

Increase in

Physical/Chemical

Treatment

CFTR 10

CFUN 1

334

Table 4.11 - PROMETHEE II Net Flows 2nd

Derivative 1690-1500 cm-1

Database


1 Un 0.969

2 Un 0.919

3 Un 0.917

4 Un 0.909

5 Un 0.893

6 MT 0.867

7 Un 0.804

8 Un 0.774

9 MT 0.718

10 Un 0.699

11 Un 0.690

12 Un 0.679

13 Tr 0.663

14 MT 0.606

15 Tr 0.575

16 MT 0.568

17 CF13 0.564

18 MT 0.543

19 CF18 0.539

20 MT 0.524

21 CF11 0.510

22 Tr 0.497

23 MT 0.485

24 Un 0.461

25 MT 0.445

26 MT 0.439

27 Un 0.421

28 MT 0.414

29 Tr 0.407

30 Tr 0.397

31 Tr 0.363

32 Tr 0.355

33 MT 0.351

34 Un 0.340

35 Tr 0.338

36 MT 0.333

37 MT 0.324

38 Tr 0.279

39 CF16 0.269

40 Un 0.266

41 Tr 0.262

42 Un 0.258

43 MT 0.256

44 MT 0.255

45 Un 0.253

46 MT 0.249

47 CF19 0.236

48 CF103 0.227

49 Tr 0.220

50 CF17 0.215


51 Tr 0.210

52 Tr 0.202

53 Tr 0.186

54 Un 0.178

55 CF110 0.178

56 CF102 0.176

57 Tr 0.173

58 MT 0.171

59 Tr 0.163

60 MT 0.156

61 Tr 0.156

62 Tr 0.152

63 MT 0.152

64 MT 0.149

65 MT 0.148

66 MT 0.140

67 MT 0.140

68 Tr 0.136

69 Tr 0.133

70 Tr 0.130

71 MT 0.121

72 MT 0.120

73 MT 0.115

74 MT 0.107

75 Un 0.096

76 Tr 0.092

77 MT 0.084

78 MT 0.072

79 MT 0.064

80 MT 0.058

81 MT 0.055

82 Tr 0.054

83 MT 0.046

84 Tr 0.042

85 MT 0.039

86 MT 0.032

87 MT 0.025

88

0.024

89 MT 0.023

90 MT 0.016

91 MT 0.015

92 MT 0.013

93 Tr 0.012

94 MT 0.010

95 MT 0.010

96 MT 0.008

97 Tr -0.002

98 Tr -0.006

99 Un -0.029

100 MT -0.033

335



101 Tr -0.034

102 Tr -0.035

103 CF105 -0.053

104 MT -0.055

105 MT -0.056

106 Tr -0.061

107 MT -0.085

108 CF14 -0.085

109 Tr -0.086

110 Tr -0.086

111 CF1011 -0.087

112 MT -0.093

113 MT -0.099

114 CF12 -0.111

115 Tr -0.113

116 MT -0.115

117 CF15 -0.116

118 Tr -0.117

119 MT -0.125

120 MT -0.126

121 MT -0.126

122 Tr -0.131

123 MT -0.140

124 Tr -0.156

125 Tr -0.157

126 MT -0.157

127 MT -0.160

128 Tr -0.163

129 MT -0.174

130 MT -0.179

131 MT -0.181

132 Tr -0.189

133 MT -0.196

134 Tr -0.196

135 MT -0.197

136 Tr -0.214

137 Tr -0.221

138 Tr -0.227

139 CF104 -0.233

140 MT -0.234

141 MT -0.236

142 MT -0.239

143 Tr -0.240

144 Tr -0.241

145 Tr -0.242

146 CF101 -0.246

147 Tr -0.258

148 MT -0.258

149 MT -0.259

150 MT -0.261


151 MT -0.263

152 MT -0.263

153 Tr -0.266

154 Tr -0.279

155 MT -0.280

156 MT -0.280

157 Tr -0.280

158 Tr -0.282

159 Tr -0.282

160 MT -0.286

161 Tr -0.288

162 Tr -0.295

163 MT -0.296

164 Tr -0.308

165 Tr -0.317

166 Tr -0.325

167 CF109 -0.328

168 Tr -0.331

169 MT -0.331

170 MT -0.357

171 MT -0.366

172 Tr -0.371

173 Tr -0.386

174 MT -0.391

175 Tr -0.393

176 Tr -0.395

177 CF1010 -0.399

178 CF106 -0.404

179 MT -0.408

180 Tr -0.411

181 Tr -0.411

182 MT -0.412

183 MT -0.42

184 Tr -0.425

185 MT -0.430

186 CF108 -0.434

187 Tr -0.496

188 Tr -0.515

189 MT -0.515

190 MT -0.522

191 Tr -0.534

192 Tr -0.540

193 MT -0.595

194 CF107 -0.619

195 Tr -0.638

196 Tr -0.706

197 MT -0.717

198 Tr -0.757

199 Tr -0.757

200 MT -0.905

336

Δ 100 %

Figure 4.22 - GAIA analysis of the 200 second derivative spectra for the

1690-1500 cm-1

hair fibre database; ▲ untreated fibres, ■ chemically treated fibres, ■

mildly treated hair fibres, ● pi (Π) decision-making axis, and ■ PC1 and PC2 criterion

variables using a Gaussian preference function.

Untreated

Mild Treatment

Chemically Treated

a forensic investigation of single human hair …i a forensic investigation of single human hair...

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