use of fourier transform infrared spectroscopy for the classification and identification of bacteria
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All Graduate Theses and Dissertations Graduate Studies
5-2007
Use of Fourier Transform Infrared Spectroscopy for the Use of Fourier Transform Infrared Spectroscopy for the
Classification and Identification of Bacteria of Importance to the Classification and Identification of Bacteria of Importance to the
Food Industry Food Industry
Sarah Pegram Utah State University
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USE OF FOURlER TRANSFORM INFRARED SPECTROSCOPY
FOR THE IDENTIFICATION OF BACTERlA OF
IMPORTANCE TO THE FOOD INDUSTRY
by
Sarah Pegram
A thesis submitted in partial fulfillment of the requirements for the degree
of
MASTER OF SCIENCE
m
Nutrition and Food Sciences
UTAH STATE UNIVERSITY Logan, Utah
2007
Copyright © Sarah Pegram 2007
All Rights Reserved
11
111
ABSTRACT
Use of Fourier Transform Infrared Spectroscopy for the Classification and Identification
of Bacteria of Importance to the Food Industry
by
Sarah Pegram, Master of Science
Utah State University, 2007
Committee Chairman: Bart Weimer Department: Nutrition and Food Science
The aim of this work was to use Fourier Transform Infrared Spectroscopy to
characterize and identify bacteria of particular significance to the food industry. FT-IR
spectroscopy is a rapid technique that can be applied to all groups of bacteria. The two
objectives were to determine a suitable sampling procedure to record a spectrum and to
determine a suitable statistical technique to identify characteristic regions of the spectrum
associated with the genus and, potentially, the species. Pure cultures ofbacteria were
grown in broth, suspended in saline and dried to produce a film on a halide salt crystal.
These films were then used to produce FT-IR spectra. In total, 80 spectra were recorded
from seven genera, seven species and four strains ofbacteria. Some of the spectra were
considered to be too low in intensity to be included in statistical analysis. Data points
from three specific windows of the remaining spectra were used to determine spectral
distances between spectra. These spectral distances were used to perform cluster analysis
using Ward's method, the Complete Linkage method and the Centroid method. The
IV
statistical analysis created successful clusters for several of the species used but was
inconclusive overall in being able to distinguish between spectra at the genus, species and
strain level. This may be due to inconsistent growth of bacteria and insufficient
manipulation of the data. This study has shown the potential for FT-IR spectroscopy to be
used to identify bacteria with significance for food but further development is needed to
reproduce the consistent results demonstrated in current literature.
(104 pages)
ACKNOWLEDGMENTS
I would like to thank my committee, Drs. Bart Weimer, Marie Walsh and Donald
McMahon, for their all-around help and persistence throughout the time it has taken me
to complete this thesis, including supplying the bacteria samples, giving me access to
laboratory facilities and seeing me through to the end of the process. Gist-brocades of
Millville, Utah also supplied bacteria samples that were used in initial trials. Thank you
to Dr. Joseph Irudayaraj for initiating this project.
I would also like to thank my friends and family who have suppmied me in every
way during my time at Utah State University. I could not have done it without your love
and prayers.
v
Sarah Pegram
VI
CONTENTS
Page
ABSTRACT ......................................................................................... iii
ACKNOWLEDGMENTS ....... . ......... ... ..... .. ... .. ........... ........... .. .... ... .......... v
LIST OF TABLES ... . ....................... . .. . .............. .. . ....... ............. .. . ... ....... vii
LIST OF FIGURES . .......................... .. . ... . ... ................................... . ... . ... . viii
LITERATURE REVIEW ........................................................................... 1
Introduction .................................................................................. 1 Fourier Transform Infrared Spectroscopy in Food Analysis ....... ... ...... .... .... .. 3 General Methods for Detection and Identification of Bacteria ....................... 7 Fourier Transform Infrared Spectroscopy Applications in Microbiology .......... 13 Fourier Transform Infrared Spectroscopy in Characterizing Bacteria . . . . . . . . . . . . 17 Statistical Analysis of Spectra.. .... ...... .. ....... .......... . . .... .. ..... ... ..... ... 24
HYPOTHESIS AND OBJECTIVES....... . ... . ...... ........ . . ............................. ... 31
METHODS .......................................................................................... 32
Obtaining Bacteria ................... ......... .................... . .... .... .......... . ..... 32 Sampling Techniques ..... .... ... ... .... .. ... . ........... . ..... ..... ..... . . ...... ..... . . .. 32 Recording of Spectra ............... ............. ...... . .. . .. ................. .... . . ....... 34 Statistical Analysis of Spectra ............................................................ 34
RESULTS AND DISCUSSION .. .. ... . .... ... ...... . .. ... . .. ............... . .................... 36
Results ........................................................................................ 36 Statistical Analysis of Spectra ............................................................ 45 Interpretation of Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
CONCLUSION ... . ......... .... .... ..... ...................... .. ................................... 53
REFERENCES ...... . ......................... .... ............................... . ........ ... ....... 55
APPENDICES ...................................................................................... 57
APPENDIX A: Tentative Assignment of Bands in Bacterial Spectra ..... . ... ...... 58 APPENDIX B: Full Set of Spectra Recorded .................... ..................... .... .... ... ... 61 APPENDIX C: Spectral Distances ........................................................ 89
Vll
LIST OF TABLES
Table Page
1 Bacteria Used in Study ................................................................. 33
A 1 Tentative Assignments of Significant Bands in Bacterial Spectra ................ 62
Cl Spectral Distances ............ .. . .. . ............ . .................... ......... ... .... .... 93
C2 Codes Used in Spectral Distances Tables ............................................. 98
Vlll
LIST OF FIGURES
Figure Page
1 Spectra of sub-samples of Pediococcus pentosaceus, replicate 1 . .... ........ ..... 37
2 Spectra of sub-samples 1 and 2 of Escherichia coli, replicate 1 . . . ... . . . ............ 39
3 Spectra of sub-samples of Pediococcus pentosaceus, replicate 2 ... . ......... ... ... 40
4 Spectra of Pseudomonas jluorescens AFT 29 and Pediococcus pentosaceus ... .. 42
5 Spectra of Lactobacillus casei 201 and Lactobacillus helveticus .. ....... ... . .... ... 43
6 Spectra of Pseudomonas jluorescens AFT29 and Pseudomonas jluorescens B52 ..... ..... .. ......... .. . . ... .... .. .. ..... . ..... .. .. ................. .... ...... . ....... . 44
7 Dendrogram produced by Ward's Method .. . .. ... .. . .. ... . . . ... .. . . ........ . .... ...... 47
8 Dendrogram produced by the Centroid Method ....................................... 48
9 Dendrogram produced by the Complete Linkage Method . ... ...... . . . .. . . .. .. .... .. 49
AI Sample spectra showing tentative assignment of peaks and windows used for analysis ...................................................................................... 60
B 1. Spectra of sub-samples of Bacillus subtilis, replicate 1 .............................. 62
B2. Spectra of sub-samples of Lactobacillus casei 202, replicate 2 . .. . ... ... .. . .. .. .. .. 63
B3. Spectra of sub-samples of Lactobacillus casei 202, replicate 3 ..................... 64
B4. Spectra of sub-samples of Pseudomonas jluorescens AFT 29, replicate 1 . . . .. ... 65
B5. Spectra of sub-samples of Pseudomonas jluorescens AFT 29, replicate 2...... .. 66
B6. Spectra of sub-samples of Pseudomonas jluorescens B52, replicate 1 . .. .......... 67
B7. Spectra of sub-samples of Pseudomonas jluorescens B52, replicate 2 .............. 68
B8. Spectra of sub-samples of Lactococcus lac tis ssp. cremoris, replicate 1 ............ 69
B9. Spectra of sub-samples of Lactococcus lactis ssp cremoris, replicate 2 ............ 70
IX
B 10. Spectra of sub-samples of Lactococcus lactis ssp. cremoris, replicate 3 .... .. . . .. 71
B 11. Spectra of sub-samples of Lactococcus lactis ssp. cremoris, replicate 4. .. .. . .. .. 72
Bl2. Spectra of sub-samples of Leuconostoc, replicate 1. ......... .. .... .. .. ..... . .. ... .. 73
B 13. Spectra of sub-samples of Pediococcus pentosaceus, replicate 1 .. .. ... . .......... 74
B 14. Spectra of sub-samples of Pediococcus pentosaceus, replicate 2 . .... ........ . .. .. 75
Bl5. Spectra of sub-samples of Pediococcus pentosaceus, replicate 3 . . . . . . . .... . .. . ... 76
B 16 Spectra of sub-samples of Escherichia coli ....... . ............. . .. . ....... .. ...... . ... . 77
B 17 Spectra of sub-samples of Pediococcus acidiladici, replicate 1 ...... . . .. . . . . . ... . .. 78
Bl8 Spectra of sub-samples of Pediococcus acidiladici, replicate 2 ..... ...... . .. . ...... 79
B 19 Spectra of sub-samples of Pediococcus acidiladici, replicate 3 ....... ... .. ... . . . . .. 80
B20 Spectra of sub-samples of Lactococcus !act is ssp lactis, replicate 1 ... . . . ... . . . .. .. 81
B21 Spectra of sub-samples of Lactococcus lac tis ssp. lac tis, replicate 2 .. .... . .. . ..... 82
B22 Spectra of sub-samples of Lactococcus lactis ssp lactis, replicate 3 . .... . .. .. . . . ... 83
B23 Spectra of sub-samples of Lactococcus lactis ssp. lactis, replicate 4 . .. . . . ... ... . .. 84
B24 Spectra of sub-samples of Lactobacillus casei 201, replicate 1 .... . .. . . .. ... . .. . ... 85
B25 Spectra of sub-samples of Lactobacillus casei 201 , replicate 2 ... . ....... . .. .. ..... 86
B26 Spectra of sub-samples of Lactobacillus helveticus, replicate 1 . .......... . ..... . .. . 87
B27 Spectra of sub-samples of Lactobacillus helveticus, replicate 2 .. .... . . .. . . . .. .. . .. . 88
LITERATURE REVIEW
Introduction
Characterization of microorganisms, including the detection, identification, and
susceptibility against antibiotics, belongs to the most frequent tasks of a wide variety of
microbiological laboratories including the food industry. Identification of bacteria is of
great importance when considering foodstuffs. It is important to determine the types of
bacteria present, some of which are harmful and others that are useful in the production
of foods.
The dairy industry relies on microorganisms for processes in production, such as
maturation in cheese production. It is also concerned with spoilage bacteria that need to
be identified and monitored to maximize product quality. In the meat industry there is
potential for microbiological contamination from external surfaces of the animals, which
is inevitably found on finished carcasses going into chill and the resulting meat products.
Although organisms that cause concern for public health are sometimes present, a more
persistent problem is spoilage caused by organisms and the ensuing economic losses.
As a result, microbiological testing is done at a variety of stages in the production
processes including the assessment of the bacteriological quality of herd bulk raw milk
and the bacteriological examination of heat-treated milk, milk products and meat
products. According to one source, the average number of tests performed for food
pathogens by one research scientist was 12,786 per year (Collins et al., 2004). With
recent increases in food borne illness and institution of HACCP the number of pathogen
tests per year are likely to be much larger.
2
Rapid microbe detection methods are required that can be used at different stages
of production. To date, conventional techniques include traditional biochemical and
serological tests and fluorescent antibody techniques. These techniques can be
cumbersome and slow requiring several tests to identify bacteria even to the genus level
and each taking several days. Kits have improved the efficiency of the tests but are
expensive ($3-5 per sample) and are available only for select bacteria (Collins et al.,
2004). Automated procedures have been developed and are available for use in research
but none are used widely. Some have been developed for industrial use but each has their
limitations. For example, gas chromatography and protein electrophoresis are available
but these require the extraction and manipulation of bacterial constituents prior to
analysis, which can be time consuming (Naumann et al., 1991). Due to the demand for
faster, more reliable, specific and cost-effective techniques for microbiological analysis,
new methods should meet the following criteria: 1) rapid and specific identification of all
bacteria on fairly small amounts, 2) simple and uniform operating procedure, 3)
differentiation strains, and 4) simple standardization and complete computerization.
Although still in the developmental stage, Fourier transform infrared (FT-IR)
spectroscopy promises to meet these criteria.
Infrared spectroscopy has been employed in the past as a technique to characterize
microorganisms according to these criteria (Haynes et al., 1958). However, this work was
severely limited by the instrumentation available and infrared spectroscopy was
dismissed as a viable technique. With more recent developments in FT-IR technology,
work has been carried out that has proved to be much more successful. FT-IR
spectroscopy is a nondestructive technique that requires minimal sample preparation, and
3
allows the rapid characterization of structural features of complex, polymeric material.
The infrared (IR) portion of the spectrum is rich in information regarding the vibrational
and rotational motions of atoms in molecules. Specific IR absorption frequencies can be
assigned to particular types of covalent bonds, and modifications of these bonds by the
local electronic environment can be detected in the details of the spectrum. Bacterial
specimens produce spectra that are unique "fingerprints" and can be used to identify
bacteria down to the strain level.
Since its development, FT-IR spectroscopy has been used to characterize a variety
of microorganisms although work has mainly been limited to clinical analysis and
pathogens (Helmet al. , 1991; Holt et al. , 1995). In the food industry, FT-IR spectroscopy
was developed for a variety of uses including protein and moisture analysis in wheat
(Osborne et al. , 1982). FT-IR spectroscopy has been used to identify lactobacilli in beer
(Curk et al., 1994) otherwise very little work has been done on the characterization of
microorganisms in the food industry using this technique. There is therefore an
encouragingly large scope for work on the use ofFT-IR spectroscopy for the
characterization of microorganisms, particularly relating to the food industry.
Fourier Transform Infrared Spectroscopy in Food Analysis
Fourier Transform Infrared spectroscopy has been used extensively by chemists to
identify compounds and used more recently in a wide variety of applied fields. Food
analysis is one such field and work has been done involving quantitative and qualitative
analysis of food components.
4
van de Voort (1992) produced a comprehensive review of the different
applications of FT-IR in food analysis. In milk analysis mid-IR is routinely used for
payment, dairy herd recording and quality control (van de Voort, 1992). Analytical speed
is a function of the number of components analyzed such as fat, protein, lactose and other
solid components. Each compohent requires a filter to be interposed in the IR beam to
make a measurement. The analysis of milk by FT-IR does not require a filter and has
shown to be as accurate as filter-based analyses (van de Voort, 1992) taking
approximately 12 sec per sample.
van de Voort (1992) used FT-IR to monitor the evaporation process of sweetened
condensed milk. FT-IR provides a simple means of handling such a viscous and sticky
material. Using pre-analyzed sweetened condensed milk supplied by a processor, an
ATR-based method was developed to directly measure fat, protein, and total solids (wet
basis). The analysis correlated well with the conventional analytical methods employed
by processors to monitor the evaporation process, but requires only one minute to
complete the analysis. Moisture can be determined to within ±0.5% of the values
obtained by the Mojonnier vacuum oven method. Mojonnier fat and Kjeldahl protein
values could also be predicted adequately (van de Voort, 1992).
Belton et al. (1987) used FT -IR for the analysis of confectionery products.
Confectionery products, and chocolate in particular, represent a real challenge to any
optical technique. Chocolate is dark and opaque, and on melting forms a viscous liquid.
The sampling methods used were ATR and photoacoustic spectroscopy (PAS). Two
methods of sample preparation were used. The simplest was to break off a piece of
chocolate or solidified cocoa liquor and clamp it to the A TR crystal face. The second
5
method involved using the samples in a molten state and then spreading them on the ATR
plate or introducing them to the PAS cell. In the solid state important information
regarding lipid phase changes was not destroyed, as was the case with samples in the
molten state. This can be used to distinguish between fresh and stale chocolate due to the
phase separation process that occurs during storage. However, quantitative lipid analysis
is best determined in the molten state since the values are affected by cooling and phase
separation. In a PAS spectrum a number of bands arising from fat can be seen, with the
strongest band being at 1744 cm-1 arising from C=O (ester). Other fat related bands occur
at 1477- 1400 cm-1 (C-H bend), 1240 cm- 1 and 1195- 1129 cm-1 (C-0 stretching).
Bands arising from the other components of the chocolate such as protein ( 1650 and 1540
cm-1) and sugars (a number in the region 1128 - 952 cm- 1
) can also be seen. The
resolution in these spectra is such that, in principle, all components could be quantified
simultaneously given suitable standards (Belton et al., 1987).
van de Voort et al. ( 1996) used FT-IR to determine the solid fat index (SFI) of
fats and oils. A method was developed that was capable of predicting the SFI profile of a
sample in approximately two minutes without the need for tempering. The calibration
was largely based on spectral regions that contain information related to triglyceride
weight-average molecular weight and the degree and type ofunsaturation of the fatty
acids that make up the triglyceride. The regions used for soybean samples were the CH
cis (3135- 2992 cm-1), the high frequency side ofthe carbonyl ester linkage (1790-
1759 cm-1) , the lo~ frequency side of the carbonyl ester linkage (1731- 1620 cm-1
),
which includes the weak C=C stretching cis/trans band, and the isolated trans (992 - 926
cm-1) and cis (769 -702 cm- 1
) bands. One of the limitations to the FT-IR approach for the
6
detennination of SFI is that each type of oil requires a separate calibration (van de Voort
et al., 1996).
Briandet et al. (1996) used FT-IR to discriminate between Arabica and Robusta
coffee beans used in instant coffee. With regard to its chemical composition, coffee is one
of the most complex of commonly encountered food commodities. Arabica beans are
valued the most highly because they have a more pronounced and finer flavor than
Robusta beans. It is therefore important that the species of the various coffee products
can be identified and quantified. Green coffee beans contain a wide range of different
chemical compounds, which react and interact at different stages of coffee processing to
produce a final product with an even greater diversity and complexity of structure. The
most important compounds in freeze-dried coffee are carbohydrates, minerals, caffeine,
chlorogenic acid, proteins, and lipids. An A TR sampling method was used for
compositional analysis and the discrimination was based on the different chlorogenic acid
and caffeine contents of the two species. These variations were observed in the regions
1150 - 1300 cm·1 and 1550- 1750 cm-1 and were used to successfully discriminate
between the two species (Briandet et al., 1996).
Although FT-IR spectroscopy has been used widely for quantitative and
qualitative analysis, its application for classification and identification of food related
components are minimal. This is especially true in the area of food microbiological
analysis. The following sections will review some of the current methods used for
' classification ofbacteria and detail the previous work done in the application ofFT-IR
spectroscopy to microbiological analysis.
General Methods for Detection and Identification of Bacteria
The characterization of microorganisms, including detection, differentiation,
identification, and antibiotic susceptibility, is one of the most frequent tasks of a wide
variety of microbiological laboratories. Currently, these analyses employ three main
approaches to the identification of bacteria- 'traditional', 'kit' and 'automated' (Collins
et al., 2004).
Traditional Methods
Traditional methods of identification rely on biochemical and serological tests
that usually involve incubating the culture in selective agar media or broth for up to
several days and then performing a specific test to determine the presence of a certain
species of bacteria. Several tests are required to identify bacteria by a process of
elimination. For example, the esculin hydrolysis test involves innoculating esculin
medium or Edwards' medium and incubating overnight. Organisms that hydrolyse
esculin blacken the medium. Serratia marcescens and Edwardsiella tarda are used as
positive and negative controls, respectively. A further example is the Ammonia test that
7
involves incubating the culture in nutrient or peptone broth for 5 d. A small piece of filter
paper is wetted with Nessler reagent and placed in the upper part of the culture tube. The
tube is then warmed in a water-bath and the filter paper turns black if ammonia is present.
The long incubation times are required to obtain a sufficient number of organisms that
can be detected by the biochemical tests. The cumulative time required is therefore quite
long, at least several days (Collins et al., 2004).
8
Fluorescent antibody techniques have also been developed to determine the
morphology of the bacteria and are used with or instead oftraditional serological tests .
The principle of these techniques is to label proteins, including serum antibodies, with
fluorescent dyes by chemical combination without alteration or interference with the
biological or immunological properties of the proteins. These proteins may then be seen
in microscopic preparations by fluorescence microscopy. The fluorescent dyes are
detectable in much smaller concentrations than ordinary dyes. Fluorescein isothiocyanate
is most commonly used and gives an apple-green fluorescence (Collins et al. , 2004).
Kit Methods
The more recent development of kits has reduced the amount of time and labor
required for identification of specific organisms. Most kits consist of a disposable,
multichamber device, containing 15 to 30 media or substrates, designed to identify a
specific group or species. They usually require 18-24h of incubation (Doyle et al. , 2001 ).
However, there are no universal kits and some organisms cannot be identified using these
methods. Kits may also suggest unlikely organisms. Numeric charts or computers are
often used to interpret the results by comparing the results of the tests with a library of
test results. A process of deduction using the results of the tests identifies the bacterium.
Kits can be expensive and have been developed for bacteria of medical importance but
may not apply to organisms of industrial significance (Collins et al., 2004).
Automated Methods
To meet the demand for rapid methods that shorten the time between the receipt
of a specimen and the issue of a report, automated methods have been developed that
monitor growth with various tools that change due to cellular metabolism. The
instrumentation used in these methods does not require constant attention and can be
allowed to proceed overnight. One example is Vitek, developed in 1976 by BioMerieux
(France). This system is based on the detection of microbial growth in microwells within
plastic cards. Identification cards are available for most organisms and results are
interpreted automatically within eight hours (Collins et al., 2004). Another system,
Biolog, includes non-clinical isolates in its initial database making it useful for
environmental purposes. The principle of the system is the ability of the test organism to
reduce tetrazolium violet, incorporated in the test cells, to the purple formazan . When a
carbon source is not used the microwell remains colorless, as does the control well. The
resulting pattern produces what is called a 'metabolic fingerprint', which is matched to
the Biolog database (Collins et al. , 2004).
Impedance
9
Impedance instrumentation has been developed to identify bacteria based on the
media that is required for growth. Impedance may be defined as resistance to flow of an
alternating current as it passes through a conductor and is a function of resistance,
capacitance and applied frequency. Electrodes are placed in the fluid culture medium and
the conductance and/or capacitance is measured. Microbial metabolism usually results in
an increase in both conductance and capacitance, causing a decrease in impedance. Tests
are monitored continually and when the rate of change in conductivity exceeds the user
defined pre-set criterion the system reports growth (Collins et al., 2004).
Flow Cytometry
10
Flow cytometry allows cell by cell analysis of the test samples and, coupled with
fluorescent labels, provides a rapid and automated method for detecting microbial flora
and examining its metabolic state. The sample is injected into a 'sheath fluid', which
passes under the objective lens via a hydrodynamic focusing flow cell. The sheath fluid
passes continuously through the flow cell, thereby focusing the sample stream into a
narrow, linear flow. The sample then passes through a light beam, which causes suitably
labeled cells to emit fluorescent pulses. Each pulse is detected and subsequent analysis
allows pulses to be recognized as separate counts and graded in terms of fluorescence
intensities. Flow cytometry analyses and sorts cells into defined populations on the basis
of cell size, density and discriminatory labelling. However, it is primarily designed for
bacterial counts of specific species rather than classification or identification (Collins et
al. , 2004).
Pyrolysis Mass Spectrometry
Pyrolysis mass spectrometry has considerable potential for the identification,
classification and typing of bacteria. Samples are spread on V -shaped Ni-Fe pyrolysis
foils held in pyrolysis tubes and heated in a vacuum using Curie-point techniques. This
causes pyrolysis in a controlled and reproducible manner. The gas produced is passed
through a molecular beam and analyzed by a rapid scanning quadrupole mass
spectrometer to produce a fingerprint of the original sample. The system is entirely
automated, with sample loading, extraction, indexing to next sample and data collection
controlled by computer. Routine analysis takes 90 s per sample with a batch of 300
samples. PMS is limited by requiring the organisms to be originally cultured on media
11
that do not impose stress and consequent alteration of phenotype expression, which may
obscure the relatedness of isolates. It has also had limited industrial application and is
currently used only for pathogens (Collins et al., 2004).
DNA Probes
DNA probes have been developed as a universal tool for identification of
bacteria, fungi , or protozoa and their application in diagnosis and epidemiology of
infectious diseases as well as in industrial and environmental microbiology. A DNA
probe is a piece of single-stranded DNA which can recognize and consequently hybridize
with a complementary DNA sequence. The probe also carries a label which 'lights up'
after hybridization. Probe specificity depends on whether it is made from total cellular
DNA, short-chain oligonucleotides or by cloning DNA fragments which gives greater
specificity. There are many assay formats, although all follow the same principle
whereby the DNA double helix of the target organism is separated, usually by heating,
and immobilized on a membrane. Immobilization prevents the complementary strands re
hybridizing and allows access for the probe. After challenge with the probe the
membrane is washed to remove any unbound probe and the resulting hybridization is
visualized using dot-blot or colony-blot assays (Collins et al., 2004)
DNA fingerprinting, also known as restriction fragment length polymorphism ·
(RFLP) analysis, combines probe and restriction enzyme technologies. The assay format
is generally known as Southern blotting. Restriction enzymes cut DNA into particular
fragments that can be used to produce a DNA fingerprint characteristic of the source
DNA (Collins et al., 2004).
12
To aid the use of DNA probes, Polymerase Chain Reaction (PCR) technology can
be used for DNA amplication. This is based on the repetitive cycling of three simple
reactions: denaturation of double-stranded DNA, annealing of single-stranded
complementary oligonucleotides and extension of oligonucleotides to form a DNA copy.
The conditions for these reactions vary only in incubation temperatures, all occurring in
the same tube in a cascade manner. Twenty cycles can take as little as 2h and increase the
amount of target DNA by a millionfold (Collins et al., 2004).
Recent development of highly efficient thermocyclers has allowed PCR
technology to be used in the rapid identification of bacterial species. Fohlman et al.
(2004) used 16sRNA-PCR to identify Haemophilus influenzae and Neisseria
meningitides. Using superconvection with ultracentrifugation high-speed PCR, results
can be obtained within 10 minutes and the amplificate can then be analyzed by DNA-
sequencing to achieve species identification. However, use of this technique for routine
analysis depends on the development of simple and fast procedures for nucleic acid
extraction (Fohlman et al., 2004). "Rapid-cycle real-time PCR" is now available in
several commercially available instruments for clinical microbiology (Collins et al.,
2004).
Fourier Transform Infrared Spectroscopy Applications in Microbiology
After the initial attempts of using traditional infrared spectroscopy for
microbiological analysis, more extensive and successful work has been carried out using
FT-IR spectroscopy. Haynes et al. (1958) carried out work using infrared spectroscopy to
analyze microorganisms. Their work distinguished between two groups of bacteria based
13
on whether the spectra displayed a particular peak (5.7 11) strongly or not at all. The
majority of spectra that displayed the peak belonged to the genus Bacillus. All strains of
Bacillus cereus produce a spectrum containing a peak but some strains of Bacillus
megaterium and Bacillus licheniformis did not produce spectra containing the peak.
Further investigation revealed that the peak was not produced by any of the aerobic
sporeformers, if grown in the absence of glucose, galactose, glycerol, fructose or other
satisfactory precursor. Two subtypes of Bacillus megaterium were also observed based
on the effect of culture age upon the spectrum. In one subtype, the peak reached
maximum absorption with a 12 hour culture but with the other subtype, the peak reached
maximum absorption after seven days (Haynes et al., 1958).
The work was limited to basic observations until the development of FT-IR.
Naumann et al. (1982) used FT-IR as a tool for probing bacterial peptidoglycan. The
peptidoglycan (murein) ofbacterial cell walls surrounds nearly all bacteria as a protecting
network. This network is probably mono-layered in gram-negative bacteria but in gram
positive bacteria it is probably multi-layered and thus constitutes up to 60% of the cell
wall weight. Because of its unique structure and function in protecting bacteria against
osmotic lysis, pepetidoglycan has been proved to be an ideal target for chemotherapeutic
attack.
Its primary structure consists basically of disaccharide-pentapeptide subunits with
unusual features such as the occurrence of alternating D and L-amino acids and a y
bonded D-glutamic acid residue. In contrast, there is no accepted model for the three
dimensional model of peptidoglycan except for evidence for and against it having a
structure similar to that of chitin or cellulose. Naumann et al. (1982) demonstrated that
14
infrared spectroscopy could contribute to a more detailed insight into the architecture of
peptidogl yean.
The results showed that peptidoglycan samples of all different chemotypes reveal
common principles. of their three-dimensional architecture in spite of the multiplicity of
primary structures. This was demonstrated by the conformation-sensitive amide bands
being constantly centered on approximately 3300 cm·1, 3080 cm-1
, 1657 cm-1 and 1534
cm· 1 (Naumann et al., 1982). The deviating features in the infrared spectra of
peptidoglycans isolated from different bacterial types seem to be useful for identification
purposes by means of a fingerprint method. Some well-defined infrared bands could be
employed for semi-quantitative cell wall analysis, such as determination of cross-linking
indices. The following characteristic variations could be detected: (a) the presence or
absence ofbands near 1730 cm- 1, 1600 cm-1 and 1400 cm-1
, (b) difference in relative
absorbance values of amide bands at 1800- 1500 cm- 1, and (c) drastic variations of
absorbance near 1730 cm-1• The 1730 cm- 1 band in Bacillus subtilis, Micrococcus luteus
and Corynebacterium spectra was ascribed to the carboxylic acid groups. The 1600 cm-1
and 1400 cm· 1 bands in Escherichia coli spectra were ascribed to carboxylate ions. This
was demonstrated by titrating with hydroxide and acetic acid, respectively, which
removed the bands from the spectrum. This information was used to estimate the amount
of cross1inking. The distinct and characteristic differences between the infrared spectra of
chitin and peptidoglycan allowed the rejection of all hypotheses based on a nearly
crystalline chitin-like three-dimensional structure of murein (Naumann et al., 1982).
Nichols et al. ( 1985) used FT-IR to analyze bacteria, bacteria-polymer mixtures
and biofilms. Analytical biochemical methods involving the identification of specific
15
"signature" or "biological marker" lipids have been used to determine microbial biomass
and community structure. Such biochemical techniques provide qualitative and
quantitative insights into environmentally relevant problems but are destructive and time
consuming. FT-IR analysis can complement or supplement existing analytical procedures
for the characterization of microbial samples, including biofilms. However, use ofFT-IR
as the sole identification tool is limited.
Two groups of bacteria were analyzed using diffuse reflectance FT -IR (DRIFT)
spectroscopy. Group I contained Pseudomonas jluorescens, Desulfovibrio gigas,
Staphylococcus aureus, Clostridium perfringens, Escherichia coli, Methylobacterium
organophilium (grown on methane) and Methylosinus trichosporium together with other
sulphate-reducing bacteria and the spectra were found to be similar. Strong amide I
(between 1690 and 1650 cm-1) and amide II (1550 cm- 1
) bands were present in all these
organisms. Other prominent bands were present at 3300 cm-1 (OH stretch), 2950 cm·1
(CH stretch), 1450 cm- 1, 1250 cm-1 and 1090 cm- 1 (Nichols et al., 1985)
Group II contained Bacillus subtilis, Methylobacterium organophilium, and
Nitrobacter winogradskyi which when analyzed showed spectral features distinguishing
them from the organisms listed above. In addition to the spectral bands noted above, the
FT-IR spectrum of Bacillus subtilis contained an enlarged OH stretch and a large
carbonyl peak centered at approximately 1740 cm-1• When grown on methanol, the FT-IR
spectrum of Methylobacterium organophilium contained a carbonyl stretch at 1740 cm-1
in addition to the strong amide I and II bands present when grown on methane. The
carbonyl stretch was the largest peak detected in the spectrum of Nitrobacter
winogradskyi and additional spectral features were observed which were not present in
the spectra of other microbes studied.
16
A difference spectrum obtained by the subtraction of a representative spectrum of
a group I bacterium from a group II bacterium spectrum showed potential for FT-IR to
enhance the differences betweeri two organisms. The data suggested that the potential
also exists for these differences to be quantified and that such techniques may be
applicable to the recognition of differences in microbial community structure (Nichols et
al., 1985).
Nichols et al. ( 1985) also studied corrosion processes to determine the role of
microorganisms using DRIFT spectroscopy. IR spectra of the bacterial cellular and extra
cellular material could be easily obtained. This indicates that DRIFT spectroscopy of the
metal disc surfaces may represent one technique to study changes occurring at the metal
water interface and to detect the presence of bacteria and biofilms.
Hedrick et al. (1991) used FT-IR for the rapid differentiation ofarchaebacteria
from eubacteria in lipid preparations. Increasing interest in biotechnological applications
of archaebacteria and their enzymes has led to the isolation of thousands of new strains
from, for example, deep-sea hydrothermal vents . The number of isolates and
archaebacterial species that do not fit into known groups, such as alkaphiles and sulfate
reducers, suggested a need for rapid routine methods to accurately classify isolates as
archae bacteria or eubacteria. The isoprenoid ether lipids of archae bacteria and the ester
.linked long-chain fatty acids of eubacteria reliably distinguish these groups with the
exception of a eubacterium which contains both ester and ether linkages.
17
The method was reliable using as little as 1 mg dry wt cell material. The lipid
extraction and degradation procedures were simplified enabling twenty samples to be
prepared in one workday. This compares with ten samples in four days for routine gas
chromatographic analysis. IR spectra were obtained by DRIFT spectroscopy. Strong acid
methanolysis of total lipid extracts was used to reduce the interfering peaks in the IR
spectra. The ratio of the peak height of ester carbonyl stretch at 1743 cm·1 to the largest
peak in the spectrum (the methyl stretch at 2924 cm. 1) was chosen to distinguish
archaebacteria from eubacteria. The method reliably distinguished three species of
archae bacteria and four species of eubacteria, which had ratios of approximately 10% and
60%, respectively. The method used was good for isolates but not appropriate for mixed
cultures or environmental samples (Hedrick et al., 1991 ).
Fourier Transform Infrared Spectroscopy in Characterizing Bacteria
Initially, FT-IR spectroscopy was used to produce crude classification schemes
involving whole groups of genera and component analysis. Further work was carried out
to investigate the ability of FT-IR spectroscopy to distinguish bacteria down to the strain
level and to use the spectra produced as a method of classification and identification.
Naumann (1984) used FT-IR to study.intact, living bacteria cells and related cell-
wall fragments. They used conventional absorbance/transmittance liquid cells equipped
with 6-12 MID Sn spacers and BaF2 or CaF2 windows. Cell suspensions were injected into
theIR liquid cell. Normalization of spectra to equal absorbance of amide I (spectral
region where signal/noise ratio is high due to strong characteristic absorbance of water)
resulted in a reproducibility of spectral features better than ± 5%. IR spectra of living
bacterial cells strongly depend on growth medium and time of growth. Consequently,
extremely precise metabolic control and strict standardized handling of all samples was
necessary to yield sufficient reproducibility and to avoid pitfalls when interpreting and
comparing IR spectra of various bacterial cells.
18
Some bands were tentatively assigned: carbonyl stretching of ester groups (1745
crn- 1), amide I (1645 crn- 1
), amide II (1547 crn-1), asymmetric stretching of phosphate
groups (1240 crn-1) and complex vibrational modes of the various polysaccharides (1200-
1000 crn-1). Naumann (1984) concluded that spectral variations accompanying cell
growth may readily be pursued, that it is possible to characterize metabolic influence of
different media of growth and that chemical and antibiotic treatment of cells may be
studied spectroscopically via FT-IR (Naumann, 1984).
In another study, Naumann et al. (1990) used FT-IR spectroscopy for the
classification and identification of bacteria. The technique involved using 10-60 )..l.g dry
weight of late-exponential-phase cells suspended in 30 j..tl distilled water. An aliquot was
transferred to zinc-selenite (ZnSe) optical plate and dried under a moderate vacuum to a
transparent film suitable for absorbance/transmission FT-IR measurements. The spectral
range was 4000 crn-1 to 500 crn-1 and was broken down into five spectral windows
containing distinctive features:
Window 1 (WI)
Window 2 (W2)
Window 3 (W3)
Fatty acid region I, 3000- 2800crn-1
Amide region, 1800 - 1500crn -I
(Mixed region, 1500- 1200crn-1)
Fatty acid region II, 1500- 1400crn-1
Window 4 (W4)
Window 5 (W5)
Polysaccharide region, 1200- 900cm-1
True Fingerprint region, 900- 700cm-1
19
The "fingerprint region" contained specific spectral patterns unique to each strain of
bacteria although the peaks are as yet unassigned to cellular components or to functional
groups.
The resulting spectra were parameterized and then analyzed by estimating and
using the Pearson's product moment correlation coefficient as a measure of similarity
between spectra. These values were then used in clustering procedures using Ward's
algorithm. The resulting classification was in good agreement with conventional
classification schemes. For example, all of the staphylococci tested formed a dense
cluster, which was subdivided into two subclusters containing the coagulase-positive and
the coagulase-negative strains, respectively. They concluded that FT-IR spectroscopy can
classify bacteria at levels of taxonomic discrimination without any pre-selection of strains
by other taxonomic criteria. In contrast to polyacrylamide gel electophoresis (PAGE) and
DNA-DNA hybridization, FT-IR is useful at the serogroup, species and genus levels
(Helmet al., 1991). Data acquisition is also faster than PAGE and other techniques with
100 spectra, transfer of data to the computer, data evaluation and cluster analysis being
performed within 2 days. Preparation is simple and quick with only 10-60 J-lg of biomass
required and no breakage and purification steps involved. In contrast to classical tests and
chemotaxonomy, FT-IR can be applied to all groups of bacteria (Naumann et al., 1990).
· Naumann et al. (1991) also used the techniques developed to establish FT-IR
spectral libraries for the identification of bacteria. Carefully selected spectra representing
20
97 different strains out of 42 species and 17 genera were used to create a spectra library.
Randomly, 72 out of 97 strains were selected to serve as "unknowns". Independently and
repetitively measured spectra (n = 5) of the unknowns were recorded. Each spectrum of
every unknown was chosen for the library challenge. The best fitting library spectra were
used to identify unknown suspensions at the genus, species and strain level and the
quotas of correct matches were calculated.
Since there is variability between spectra even when one strain is repetitively
measured it was necessary to determine the reproducibility of bacterial FT-IR spectra for
a selected strain under different culture conditions. These conditions were: different
aliquots of identical bacterial suspensions; independent preparations of bacteria grown on
agar plates from the same batch; spectra recorded over a long period oftime (two years)
for different preparations from bacteria grown on the same agar produced from different
batches. The first condition yielded excellent reproducibility, however the third condition
is more practical since it is common to use agar from the same manufacturer but different
batches over a period of time.
Each of the five spectral windows was also tested singly to determine the window
dependent quotas of correct matches. To enable comparison of different windows the
spectral distances were suitably normalized. The entire spectrum yielded only poor
quotas of correct matches: 31.9, 43.1 and 55.6% of the unknowns were identified as to
strain, species and genus, respectively. This is probably due to the parts of the spectrum
that provide no specific information. The quotas of correct matches for individual
windows ranged from 29.2% (amide region) to 80.6% (polysaccharide region) for
identity at the strain level. The most strain-specific information seemed to be contained in
the fatty acid region I, in the polysaccharide region and in the fingerprint region.
Windows were then combined to get the optimal identification quotas . The data
suggested the following combinations, where Cl - C4 are combinations of the spectral
windows shown:
(1) C1 = {W1 n W2 n W3 n W4 n W5}
(2) C2 = {W1 n W3 n W4 n W5}
(3) C3 = {Wl n W4 n W5}
(4) C4 = {W1 n W4}
The combinations C3 and C4 obtained best results (Naumann et al., 1991 ).
21
Holt et al. (1994) used FT-IR to discriminate between different species of
Listeria, an important food-borne pathogen. The method used was similar to that of
Naumann et al. (1990) where the bacterial cells were suspended in water and then an
aliquot was dried on a ZnSe crystal. For all the type species present in the set, spectra
were recorded on three separate occasions using different batch cultures. At least six
replicate spectra of each of the type species were recorded and in all, 59 spectra were
selected for analysis. Six replicate spectra of each type species, recorded on three
separate occasions using different batch cultures were considered to adequately represent
the variability present in repeated measurements of the spectra of the type cultures, and
hence they provide a test of the power of discrimination of the infrared method. The
results were analyzed using Principal Component Analysis to reduce the data set and then
Canonical Variate Analysis was used for classification (Holt et al., 1994).
22
Curk et al. (1994) used FT-IR to identify Lactobacillus species. The method again
modeled Naumann et al. (1990). The reproducibility ofFT-IR spectroscopy and the
influence of culture temperature and time on the results were first estimated. The spectral
distance, a measure of dissimilarity calculated from the Pearson's product moment
correlation coefficient, varied with the strain and spectral window used. However, the
minimal spectral distance between two spectra given by two different species was always
greater than the spectral distance within the same strain.
The influence of the culture temperature on the spectra was estimated by
comparing spectra for a strain grown at the optimal temperature (28 or 45°C) and at
37°C. The spectra for a strain grown at these two temperatures give more differences than
two spectra of the same strain grown under the same conditions. Nevertheless, the
distinction between species was often greater than the difference between a single strain
grown at different temperatures. Time in culture had a similar effect and so, although the
differences still allow discrimination between species, it is easier to keep all conditions
the same to facilitate comparisons and increase reproducibility (Curk et al., 1994).
Kirschner et al. (200 1) studied the use of FT-IR for the identification of
enterococci at the species level. They compared the discriminatory power of vibrational
spectroscopic techniques, including FT-IR, with phenotypic and genotypic methods. D.
Naumann was a member of the group conducting this study and the method used to
record the FT-IR spectra was the same as in his previous work (Naumann et al., 1990).
Seven repetitive measurements over a period of six months from independent sample
preparations of 18 strains of enterococci were performed, resulting in 126 spectra.
Statistical analysis of the spectra was carried out using OPUS software (version
3.0; Bruker). First and second derivatives of the spectra were calculated using a 9-point
Savitzky-Golay filter to enhance the resolution of superimposed bands and to minimize
problems from unavoidable baseline shifts. The first derivatives of the original spectra
were used as input for cluster ahalysis and spectral distances were used for hierarchical
clustering analysis.
23
The results indicated that FT-IR is highly reproducible and specific at the strain
level. Thus, it allows accurate differentiation of closely related bacterial species such as
enterococci. In comparison to conventional genotypic and phenotypic methods, FT-IR
was found to be accurate for a wider range of Enterococcus species and more economical
for routine analysis (Kirschner et al., 2001).
Oust et al. (2004) investigated the robustness ofFT-IR spectroscopy to distinguish
strains of bacteria under varying growth conditions. Resulting spectra were robust under
small changes in growth temperature (28-32°C), growth medium (MRS and APT),
growth time (42, 48 and 54 hours) and atmospheric conditions (aerobic and anaerobic)
with differentiation between species and strains being unaffected. Larger variations in the
growth medium only affected the differentiation of strains. However, it was also noted
that any changes in conditions resulted in larger variations in the data collected.
Therefore, to ensure reproducible results it is necessary to standardize the cultivation
conditions as much as possible (Oust et al., 2004).
24
Statistical Analysis of Spectra
The statistical analyses of the spectra recorded from the bacterial samples used in
the literature all involved the same basic principles. The spectra were prepared for
statistical analysis using parameterization. This included filtering of the spectra (using
first or second derivatives or deconvolution) to enhance the resolution and selecting a
combination of certain spectral windows rather than using the whole spectra for analysis.
The spectra were also normalized and scaled, usually using the strongest amide peak at
1656cm·1 (Naumann et al., 1990)
The statistical analysis of the spectra was performed in a variety ofways.
However, each method utilized multivariate techniques such as Principal Component
Analysis (PCA), Canonical Variate Analysis (CV A), Cluster Analysis (CA), and Factor
Analysis (FA) (Naumann et al., 1990, Curk et al., 1994, Holt et al. , 1995).
Principal Component Analysis
Principal component analysis was used as an initial treatment for the data to
reduce the data set to a more manageable size suitable for further analysis (Naumann et
al. , 1990). The data set reduction is achieved by treating each spectrum as a point in a
hyperdimensional space with as many dimensions as variables (absorbances) recorded
from the spectrum. The number of variables is usually one per wavenumber over the
entire spectrum or from selected regions of the spectrum. The total set of n spectra form a
"cloud" of n points in this hyperdimensional space that can be described by a set of p
principal components, where p is less than n. The principal components consist of
eigenvectors and eigenvalues with each successive eigenvector representing a decreasing
amount of interspectral variance. Thus, approximately, only the initial two to eight
eigenvectors are required to describe the bulk of interspectral variance with further
eigenvectors corresponding merely to noise in the spectra. This significant reduction in
the data set allows further analysis by other techniques (Johnson and Wichern, 1992).
Canonical Correlation Analysis
25
Canonical Correlation Analysis can classify the spectra by using linear
combinations of the variables (Holt eta!., 1994). The classification process considers the
correlation between the linear combination of variables in one set and those of another
set. It determines the pair of linear combinations with the largest correlation and then the
next pair from the remaining set that is uncorrelated with the first pair, and so on. The
pairs of linear combinations are called canonical variables and their correlations are
called canonical correlations. The aim is to maximize the ratio ofbetween-groups to
within-group variance and hence provides discrimination between the groups (Johnson
and Wichern, 1992).
Factor Analysis
Factor Analysis groups the variables according to their correlation so that all
variables within a particular group are highly correlated among themselves but have
relatively small correlations with variables in a different group. For example, when
considering the intelligence of different individuals by comparing class scores, one may
expect scores from chemistry, physics and math to be highly correlated but uncorrelated
with English and history. This correlation can represent an underlying, but unmeasurable,
26
factor. These factors can be used to indicate common bacterial origin (Johnson et al.,
1992).
Cluster Analysis
Cluster analysis uses the Euclidean (straight-line) distance between data points to
determine similarity between spectra and assign them to clusters. No assumptions are
made concerning the number of groups or the group structure. Hierarchical clustering
methods are either agglomerative (successive mergers) or divisive (successive divisions).
The results of both methods may be displayed in the form of a dendrogram (two-
dimensional diagram). The following are steps in the agglomerative hierarchical
algorithm for grouping N objects:
1) Start with N clusters, each containing a single entity and an NxN matrix of distances. 2) Search the distance matrix for the nearest (most similar) pair of clusters. 3) Merge these clusters to form one cluster and update the distance matrix replacing the
original distances between the remaining clusters and the separate nearest clusters, with new distances between the remaining clusters and the merged cluster.
4) Repeat 2 and 3 a total of N-1 times. All objects will be in a single cluster at termination of the algorithm. Record the identity of the clusters that are merged and the levels (distances) at which the mergers take place.
(Johnson and Wichern, 1992)
This algorithm is carried out by a statistics computer package. The algorithm can
vary according to how the distance between clusters is determined. An example is Ward's
algorithm, which clusters groups by determining the least increase in variance or error
sum of squares in the new cluster. This is calculated as the sum of the squared distances
from each member of the cluster to the center of the cluster (Johnson and Wichern,
1992).
27
Application of Statistics for Classification
Holt et al. (1994) used canonical variate analysis (CV A) to distinguish species in
the genus Listeria. The spectra were stored as normalized, scaled sets of data in the range
of2000 to 750 cm-1 at 1cm-1 sampling intervals. The variables were standardized by
subtracting their means to give a 59-by-1250 matrix, X. PCA was used for data set
reduction, producing a 59-by-r orthogonal score matrix, A, and a 1250-by-r orthonormal
matrix of loadings, B, where A = XB. It can be shown that the best rank r approximation
to X is AB' and so a larger r will retain more of the variability of the original data at the
cost of a higher dimensionality. In this case, r was chosen to be 20. The score matrix was
then used as the data set matrix for CV A. This analysis produced an r-by-m matrix of
loadings, C, leading to a 59-by-m matrix of scores, D, where D = AC which was then
used to distinguish species by observing the clusters in increasing dimensions. The
species were discriminated in four dimensions (m = 4). The loadings were defined in
terms of the original variables and used to indicate regions of the spectra that are
important for discrimination. These correspond to large, positive or negative, loadings
and occurred around 947-985cm- 1 and, to a lesser extent, 1236 and 1750cm-1 (Holt et al.,
1994)
The loadings were also used to find the canonical variance (CV) scores of new
observations and place them in the group they are closest to in CV space. Assessment of
the performance of the classification was obtained using the "leave-one-out" estimate.
This involves taking each observation out of the data set in tum, calculating the CV's and
classifying the omitted observation on the basis of these new CV's. Every observation
was correctly classified by using this method (Holt et al., 1994).
28
Naumann et al. (1991) used spectral windows as described previously. These
were analyzed using factor analysis to reduce the data set and then cluster analysis to
determine the bacterial classification at the genus, species and strain level. The
appropriate grouping ofbacterial spectra into clusters was achieved by systematically
varying some parameters prior 'to cluster analysis. These parameters concerned the
filtering of the spectra (first or second derivative), the selection and combination of
certain spectral windows and their weighting. The weighting was intended to account for
the specific contributions of some cellular compounds, e.g. fatty acids of the membrane
or polysaccharides of the cell wall.
The similarity between the spectra was determined using Pearson's product
moment correlation coefficient. This is a measure of the linear association between two
variables and is also known as the sample correlation coefficient. These coefficients were
converted to d-values or spectral distances (a measure of dissimilarity) using the
equation:
dyty2 = (1.0 - r yly2) · 1000.0
These could theoretically adopt values between 0 and 2000 (Naumann, 1985). A value of
between 0 and 10 indicates indistinguishable spectra (Naumann et al., 1990). Since the
spectra were subdivided into several spectral windows, the similarity between two spectra
was calculated as a linear weighted combination of the single similarities giving the mean
or overall spectral similarity. The single similarities were transformed to a uniform
scaling level, multiplied by the weights, co-added and divided by the sum of the weights.
The clustering was performed using Ward's algorithm (Naumann et al., 1991).
29
Curk et al. (1994) adopted the method and statistical analysis established by
Naumann et al. ( 1991) to identify Lactobacillus species including the use of spectral
windows and clustering using Ward's algorithm. Curk confirmed his identification results
using DNA-DNA hybridization techniques (Curk et al., 1994).
More recently, Oust et al. (2004) used FT-IR spectroscopy to analyse 56 strains
from four closely related species of Lactobacillus. Initially Hierarchical Cluster Analysis
was used to study the clusters in the data but the dendrogram failed to successfully
differentiate the four species. When the data was analysed with Partial Least Squares
Regression, the strains were differentiated into four clusters according to species (Oust et
al., 2004).
Alternative Methods for Analysis of Spectra
Hedrick et al. ( 1991) distinguished between the spectra of eubacteria and
archae bacteria using the ratio of the peak height of ester carbonyl stretch at 1743 cm-1 to
the largest peak in the spectrum (the methyl stretch at 2924 em-\ The archaebacteria
contain isoprenoid ether lipids and the eubacteria contain ester-linked long-chain fatty
acids. This method successfully distinguished between the two groups with the exception
of a eubacterium that contains both ester and ether linkages. The typical value of the
height of the ester carbonyl stretch peak as a percentage of the major methyl peak was
found to be approximately 10% for archaebacteria and 60% for eubacteria (Hedrick et al.,
1991).
Nichols et al. (1985) also used peak ratios to analyze bacteria. The analysis
included dividing the bacteria studied into two groups. The first group was characterized
by a dominant amide I band and the second group of organisms displayed an additional
carbonyl stretch at~ 1740 cm-1• The key band ratios correlate with compositions of the
material and provide useful information for the application of FT-IR spectroscopy to
environmental biofilm samples and for distinguishing bacteria grown under differing
nutrient conditions (Nichols et al., 1985).
30
Given the development in technology and computerized statistical analysis
techniques, FT-IR spectroscopy promises to be a very powerful technique for the
classification and identification of bacteria. This could be of great significance in the food
industry where there is a high demand for microbiological analysis in production
processes, for safety reasons and for controlling spoilage.
31
HYPOTHESIS AND OBJECTIVES
Hypothesis
FT-IR spectroscopy is a reproducible method to identify bacteria.
Objectives
1. Design a sampling procedure to provide reproducible spectra for each organism.
2. Define the unique components of the spectrum to use as an identification tool.
METHODS
Obtaining Bacteria
The bacteria were obtained as cultures from Gist-brocades, Millville, Utah and
Dr. Bart Weimer. The Gist-brocades bacteria samples were used in initial trials and
planning stages of the project. The samples provided by Dr. Bart Weimer were used in
the final study. The list of bacteria used for the study is found in Table 1.
Sampling Techniques
32
The freezer cultures were thawed and used to inoculate the respective broth. The
inoculated broth was incubated for 16-24h at the appropriate temperature (Table 1). The
broth was centrifuged (5000 rpm, 10 min) to collect the cells which were then washed
three times with saline (0.85% NaCI) and then re-suspended in 350 111 of saline. A 25 111
aliquot was placed on a crystal and dried to a transparent film in a desiccator containing
calcium sulfate, under a moderate vacuum (approximately 25 Torr) . The crystals used
were either barium fluoride or calcium fluoride and these were used interchangeably as
they are themselves transparent to the infrared radiation. The resulting film was used to
obtain a spectrum. A background spectrum was obtained using a blank saline film on the
crystal.
All spectra were recorded on an FTS-7 FT-IR spectrometer (BIORAD) using 32
scans. The spectral resolution was 8 em -I. The data point resolution was one point per
three wavenumbers, from 4000cm- 1 to 500cm-1•
Table 1 Bacteria Used in Study
Genus Species Subspecies Strain
cremoris SKll Lactococcus lac tis
lac tis S3
Leuconostoc
acidiladici 33314 ATCC Pediococcus
pentosaceus 25744
helveticus 212 Lactobacillus
casei 201,202
Bacillus subtilis
Pseudomonas jluorescens AFT29, B52
Escherichia coli PTRKL2
Growth Temperature Broth
oc
30 Elliker
20-30 Elliker
25-40 (37) MRS
30-40 Elliker
30 Nutrient
30 Nutrient
37 Nutrient
Conditions
Facultative Anaerobic
Anaerobic
Microaerophilic
Facultative Anaerobic
Aerobic
Aerobic
Facultative Anaerobic
Number of Batches
4
4
1
3 i
3 i
2 I
2 (per strain) 1
I
I
1
2 (per strain)
1
(.;.) (.;.)
34
Recording of Spectra
The first set of spectra was collected from one replicate of each bacterium used in
the study. The first set of replicates of bacteria consisted of Bacillus subtilis, E. coli,
Leuconostoc, Pediococcus pentosaceus, Pediococcus acidiladici, Lactococcus lactis ssp.
cremoris, Lactococcus lactis ssp. lactis, Lactobacillus casei 201, Lactobacillus casei
202, Lactobacillus helveticus, and Pseudomonas fluorescens B 52 and P. jluorescens
AFT 29. The bacteria were grown and analyzed as described above. Four spectra were
recorded for each bacterium.
A second set of spectra was collected using another sample of each of the
bacteria. These bacteria were chosen from those in the first set of replicates but were
grown from a second sample under the same growth conditions. The bacteria chosen
were those that successfully produced spectra from the first set of replicates. These were
P. pentosaceus, P.acidiladici, L. lactis ssp. lactis, L. casei 201, L. casei 202, L.
helveticus, P .jluorescens B 52 and P. fluorescens AFT 29. These bacteria were
considered successful because they grew sufficiently to produce spectra with clearly
distinguishable peaks of reasonable intensity rather than background noise. Four spectra
were recorded for each bacterium.
Statistical Analysis of the Spectra
As well as visual comparison of the spectra, statistical analysis was carried out to
determine if the spectra could be used successfully to distinguish between different
organisms. The total number of variables for each spectrum was equivalent to the number
of data points and the spectrometer used recorded one data point per three wavenumbers.
35
Hence, for each spectrum the raw data contained over 1100 data points. To prepare the
raw data for analysis, the number of variables needed to be reduced while conserving as
much of the spectral information as possible. To achieve the needed data reduction while
preserving the most information the spectrum was divided into windows as defined by
Naumann et al. (1990). The windows used for further analysis corresponded to the
wavenumbers 3000-2800 cm-1, 1500- 1400 cm-1 and 900-700 cm-1. The similarity
between spectra was then determined by comparing the absorption values of these ·
wavenumbers using the Pearson product coefficient to obtain correlation values between
all the spectra obtained. These correlations were converted into spectral distances, a
measure of dissimilarity, using the equation:
dyly2 = (1.0- ryly2). 1000.0
where d is the spectral distance between two spectra and r is the correlation between
those spectra. The spectral distances were then compared to see which spectra are most
similar to each other. This was done statistically using cluster analysis performed using
Ward's method, Centroid method and the Complete Linkage method (Johnson et al.
1992). Each method produced the same dendrogram.
36
RESULTS AND DISCUSSION
Results
In total 112 spectra were recorded from seven genera, nine species and two strains
for each of two species. Each sample had at least four sub-sample spectra. The spectra
were stored as sets of data in the range of 4000cm- 1 to 500cm-1 at 3cm-1 sampling
intervals. All of the spectra produced can be found in Appendix B.
The spectra produced are the result of the infrared light being absorbed by the
chemical bonds in the bacterium as each wavelength causes resonance. The strongest
absorbance peaks occur due to the following components in the sample: water (3500 cm-
1, broad), amide bonds (1700- 1500 cm-1), fatty acids (1500- 1400 cm-1
) and
polysaccharides (1200- 900 cm-1). The region below 900cm-1 is referred to as the
"fingerprint region" because it consists of several unassigned peaks, which are specific to
each chemical compound that may be related to a specific bacterium (Appendix A). The
tentative assignment of the chemical bond for other peaks frequently found in a bacterial
spectrum can be found in Appendix A.
Reproducibility of Spectra
The effort of this work was to confirm that a reproducible spectrum could be
collected. This was achieved to some degree by producing a spectrum with distinct peaks,
which could then be reproduced from the same replicate of the each bacterium. However,
the fingerprint region did vary between spectra. One example of this is the set of four
spectra collected from the first replicate of Pediococcus pentosaceus (Fig. 1 ).
r !
q ··-- - --..,
Spectrum 1
I II
1\ !i I \ ! \ ,\ , ' I \ !'\ I\
I \ i i I '
'
~ 'J"\ ~ J -.;./'V••/ \; \.-... /' I \. ,l·\ /1/\ /l 'L=.__ --, < _J'\_~:~_; , - - ,- .. -4000 3500 3000 2500 2000 1500 1000
Wavenumber (cm-1)
I .3 1
I Spectrum 3
~ .2 ]
i I < _, ...
I . !'\ oj' ' . \ :--"''~"-./ '"-... ._'\, . --- .}~\. ..... -
1\ i\ I i A " I It· r ' J \! \ ") \
)' k IV \..A , .,. jo
___ ,.., .
~-- - -··r------Y--··--··-·····T-····-----·-·""1-·······-------·· r ___ __]
4000 3500 3000 2500 2000 1500 1000 Wsvenumber(an-1)
.3 Spectrum 2
~ .2
j .1 ~
1\ i'\· i
\ I ,t, .. , l I \1 \ I \
....... I f ~~ \ / .. I \ ' ,.. I '\. !'~ I ' •.{" . \; . .! .. J, -~vv/ \ -.... ..... i'\ /'' v \q I ~~-- ;
0
~ ....... --~-~ .. ---· ·······;---· -r--------,-- - -~ ·-l -- ---. l--~ ~------~---..,....- ·---------= 4000 3500 3000 2500 2000 1500 1000
Wavenumber (cm-1)
r-·--
.3 Spectrum 4
~ .2
j .1
/ \
0 I \, ..
"·· ,._,____1\..;/ \....._,
4000 3s00 -----,--
3000
f\ I, r I~' ;'"'\ ,
!· ! \ ,, ) J ! ' I ) V a._../ \; \ / J \,,.,,, .. ,A- .. A -~~--¥~'" '-1 .. --"'/
·..--- --r· ---- ·--- r--2500 2000
r 1500 1obo
Wavenumber (cm-1)
Figure 1. Spectra of sub-samples of Pediococcus pentosaceus, replicate 1.
w --.)
38
Four separate aliquots were used and four very similar spectra with distinct peaks were
recorded. Reproducible spectra with distinct peaks were collected for P. pentosaceus,
P.acidiladici, L. lactis ssp. lactis, L. casei 201, L. casei 202, L. helveticus, P .jluorescens
B 52 and P. jluorescens AFT 29. The spectra recorded for Bacillus subtilis and
Escherichia coli had indistinguishable peaks because they did not grow as expected (see
Fig. 2).
In order to confirm the reproducibility of the spectra it was necessary to compare
the spectra of the first replicate with spectra of a second replicate of the same bacterium.
For example, the spectra of the first replicate of P. pentosaceus can be compared with the
set of spectra collected from the second replicate of P. pentosaceus (Fig. 3). Although
the intensity of the peaks is approximately 0.1 absorbance units higher in the second set
of spectra the actual shape of the peaks is the same in both sets. Some distinct features
observed in both spectra are the multiple splitting of the amide peak at 1550 em·' and the
splitting of the peak at 1100 em·'. This reproducibility was also seen in the spectra for the
second replicate of the other bacteria. Once the spectra were collected with sufficient
reproducibility, the data were examined for specific differences between the bacteria.
Specificity of Spectra
The second objective was to detem1ine if a spectrum contained specific features
that allowed determination of each bacterium down to the strain level and hence, could be
used to distinguish between closely related bacteria. The first level of distinction we
39
l
.3 -
---,--- --r .... ·--·-················--···r······-···--.. ····----······T··-······-··········- ........ _,_
4000 3500 3000 2500 2000 1500 1 000 Wavenumber (cm-1}
--- ·-------
.3
g .2·
i <(
0
.............. r ................ ··-·-·-······ .. ·,--···--:· ................. .. -T ... --···-·-... - ....... -.. 1·····-----..... .
4000 3500 3000 2500 2000 1500 1000 Wavenumber (cm-1)
Figure 2. Spectra of sub-samples 1 (top) and 2 (bottom) ofEsherichia coli, replicate 1
·~ ~ .2·
j c( .1 j\
I '"
Spectrum 1
! ];
l
l \ ~\ '·'! /' I V · , ·1 '' f 1 : I!
I \;J'-J\/ \ ,.v' l - _____ ) . -/ I I ··v,
0 1 ~~-- I --~·{\...·>~' "-.,., ____ ... _______ }\_
J L- ·-··-······
4000
.3·i
I
~ .2
i . c( .h
n ... T'''''
3500 ----·-.. ·····-···· ..................... , ....... . 3000 25,00 2000
wavenumber(cm-1)
Spectrum 3
··--;--····-··········-T
~~ ,,
1500 1000
/\ 1\ I \ 11•.'\
I \ \'
f
I ... _\.A J \t' \ ''\_
/
/ 1_, l I \ f\ !\ / \ ' ,., "·\ I W _, v \
0 ·1· ·""'-'..._/ '"-,._ 1•; I - "'-~1 ......... _~! \.. ____ ._,., ______ ; \
I ... ·--r····· ............. T_____ -- .............. r----- · ·····--- r--- --r--·--, ---4000 3500 3000 2500 2000 , 500 1000
Wavenumber (cm-1)
I .31
j
Spectrum 2
~ .2 ! /\ ~
~ .1
I,
IL I ' i\ ! \; i ,,
!. )' \ ';' \ !n v . ./'" I 'v \ j l -- ... - ~~- '· ... /\ _,j\.
;/\ ' \
o
1
- I · "J\ .. ' _ __.)-.;'\,:·/ ~- ... ,
i , ................. _ ---,.-- ................... , ..... .. 4000 3500 3000
"""¥•!·-- ---- __ T __ _
2500 2000 ,-----,--·· 1500 1000
Wavenumber (cm-1)
I Spectrum 4 .3i I
8 .2 I I c ~ jl .1 I\ I!
j \\ 'i !\ . f . I li' '
J
I "' I,, '~ I 0 , I '\ : \.;\/\ fi
l ... r'---''-"' "'··, ~ I W'- V \ JJi · , J \ I \,rr/ I
I ~_;, _____ ._ ...... J
~· ······ I
. ·-······-hr-···-··············-···1-······· ······-···T-.................. ..1
4000 3500 3000 2500 2000 , 500 1000 Wavenumber (cm-1)
Figure 3. Spectra of sub-samples of Pediococcus pentosaceus, replicate 2.
~ 0
41
examined was between the genera. This was observed using the spectra collected from P.
jluorescens AFT 29 and P. pentosaceus (Fig. 4). Although the absolute intensity of
absorbance was different, some specific peaks were observed that may distinguish
between the two genera. The peak at 1100cm-1 was split into two peaks of approximately
equal intensity in the pediococcal spectrum. In the Pseudomonas spectrum although the
same peak was split, one of the resulting peaks was more intense than the others. The
peak at 1550cm-1 showed multiple splitting in the pediococcal spectrum and appears to
be a distinguishing feature. However, it was hard to determine if this multiple splitting
appears because of the lower intensity of the absorbance or if it is due to specific bacterial
components.
The second level of distinction tested was between each species. This was
observed using the spectra collected from Lactobacillus helveticus and Lactobacillus
casei (Fig. 5). One distinguishing feature between these two spectra may be the relative
height of the peak at 1200 em -t ( 1) to the peak at 11 00 em -t (2). In the L. helveticus
spectrum peak 1 was consistently higher than peak 2. In the L. casei spectrum peak 1 was
consistently lower than peak 2.
The third level of distinction was between strains. This can be observed in the
spectra collected from Pseudomonas jluorescens AFT 29 and Pseudomonas jluorescens
B52 (Fig. 6). There are some differences between these spectra, such as a small
difference in splitting of the peak at 1100 cm-1, but overall the spectra appear to be very
similar. It is hard to distinguish between differences in the spectra that are specific to the
strains and differences that are due to other factors such variations in the intensity. It is
.3
8 .2 · c:
~ g .0 <( .1-
I 1-
4000
I .3
--~-- ·-·--t ·------. - ----,-·· ---,-··-·---.......--,-------~ 3500 3000 2500 2000 1500 1000
Wavenumber (cm-1)
···--.---.. -············· · --"""T---··-·r-- ·- -···--········T·--····--·-····r······---····-····-
4000 3500 3000 2500 2000 1500 1000 Wavenumber (cm-1)
Figure 4. Spectra of Pseudomonasjluorescens AFT 29 (top) and Pediococcus pentosaceus (bottom).
42
············· ................ -- ·-- ---- ·-··-- ------l
f\ .3- /i
II 8 .2- /\ I I\ :If\ c:
€ 0 rn .c <( .1 I \\;\ I v l }\}!\ )!'
J J, ~\f\ \ ~ I
\.""' t '\r-r-r ' o--, -'"-,_ "-...., .. , h
l._ I ---,------,-·-,_·.J·-\.._ ._--_--~-- T -----,----
4000 3500 3000 2500 2000 1500 1000 Wavenumber (an-1)
.---·- . ··--n -·· _ .. _ - ·- -·-
1 I\ ---l
,i I \ 1 21 I \\ j I .
I '\ !"' i \ ~. ! I I
\, '\ I I I
vv \/ I I
v \ 0
" ·' ' I \ \.jA.~/1 \ \
I .
o- ·VJI "'"'-.J\_ ___ . ____ J
1-- .. -T--····· - -r - - ····- -.----- -----r-...... _____ T ··-··· ·---·····---.--
4000 3500 3000 2500 2000 1500 1 000 Wavenumber (an-1)
\ I l' !I
Figure 5. Spectra of Lactobacillus casei (top) and Lactobacillus helveticus (bottom).
43
.3
!-, ----,------,------,----.-- ----,------··-~----·-· ........ .
4000 3500 3000 2500 2000 1500 1000 Wavenumber (cm-1)
.3 -
~ ~ IS .1
I," I I I ' I ;1\ IV \ ~/\ )' I I \ \ ,,/\ 'l_ __ /
0-j 1 \J"\.___ r~ 1 w " L-.-J ',. __ J l.-~ ! 1·----·--·-r-· ·----.. -r--.. --~- --·-· ·-,---.. ---··---r-·-.. -· ........ ..
4000 3500 3000 2500 2000 1500 1 000 Wavenumber (cm-1)
Figure 6. Spectra of Pseudomonasjluorescens AFT 29 (top) and Pseudomonas jluorescens B52 (bottom).
44
very difficult to detennine the differences between the spectra from different organisms
by visual inspection. Therefore, a more complete analysis was done using statistical
procedures and spectral analysis of specific regions.
Statistical Analysis of the Spectra
45
The total number of variables for each spectrum was equivalent to the number of
data points and the spectrometer used recorded one data point per three wavenumbers.
Hence, for each spectrum the raw data contained over 1100 data points. To prepare the
raw data for analysis, the number of variables needed to be reduced while conserving as
much of the spectral information as possible. To achieve the needed data reduction while
preserving the most information the spectrum was divided into windows as defined by
Naumann et al. ( 1990). The windows used for further analysis corresponded to the
wavenumbers 3000-2800 cm-1, 1500- 1400 cm-1 and 900-700 cm-1. The similarity
between spectra was then determined by comparing the absorption values of these
wavenumbers using the Pearson product coefficient to obtain correlation values between
all the spectra obtained. These correlations were converted into spectral distances, a
measure of dissimilarity, using the equation:
dyly2 = (1.0- ryly2). 1000.0
where d is the spectral distance between two spectra and r is the correlation between
those spectra. The spectral distance values can be seen in Appendix C, Tables 1 a-e. The
spectral distances were then compared to see which spectra are most similar to each
46
other. This was done statistically using cluster analysis perfonned using Ward 's method,
the Centroid method and the Complete Linkage method (Johnson and Wichern, 1992).
Ward's method and the Centroid method produced the same dendrogram (Figs. 7 and 8)
and the Complete Linkage method produced a dendrogram (Fig. 9) that was almost
identical except for the switching of two clusters (number 7, P. jluorescens and number
14, Leuconostoc).
The dendrograms created five initial clusters represented by the five different
colors, which were further subdivided into smaller clusters. The different colors might be
expected to be homogeneous with regard to the type of bacterium they contain. The
smaller the distance required to connect two organisms or clusters, the greater the
similarity between them. The graph alongside below the dendrograms indicates the most
appropriate number of clusters by measuring the level of similarity at each fusion of two
clusters. Thus, a steep rise in similarity distance, indicating a significant increase in
dissimilarity between two clusters, would indicate the most appropriate number of
clusters. It would be expected that this would occur after ten or twelve clusters relating to
the number of species or strains present in the data. However, the graph has significant
increases in dissimilarity after 20, 24 or 27 clusters indicating that the spectra for
different genera, species or strains of bacteria were not sufficiently similar to be clustered
in 10 or 12 clusters.
Taking genera, species and strain each in tum, it can be seen that of the genus
Pediococcus, three of the replicates (17, 20 and 21) are clustered quite closely together
but that the others are in different clusters. For the genus Lactobacillus, all but one of the
replicates are contained within two clusters although the similarity distance between the
47
B. subtilis •2 ,, ,,
L. casei 201 ·27 L. lactis ssp lactis •25 ~
L.helveticus •28 L. casei 202
L. casei202 a::-P. pentosaceus 1t,
L. lactis lactis a2j L. casei201 a26
~}- h _______}-- 1-
L. lactis ssp lactis a24 ~-1---
P. f/uorescens a? -Leuconostoc a14
P. pentosaceus a1? P. acidiladici a2o P. acidiladici a21
L. lactis ssp lactis a22
I J I
I -1-
P. fluorescens 06 P. pentosaceus 015
P. fluorescens 08 P. acidiladici 019
P. fluorescens 09 I
L. lactis ssp cremoris 010 -- ~
E. coli •3 Saline K30
L. lactis ssp cremoris •1 1 L. lactis ssp cremoris •12
E. coli •18
I I I
L. lactis ssp cremoris •13 I L. helveticus •29 ,,
··'
L~_.....,"" ......... :71 Figure 7. Dendrogram produced by Ward's Method.
48
B. subtilis •2 ,,
L. casei 201 ·27 L. Jactis ssp Jactis •25 ~
L:helveticus •28 L. casei202 a4 L. casei 202 as
P. pentosaceus a 16 L. lactis /actis a23
L. casei201 a26 -L. lactis ssp Jactis a24 r---
P. f/uorescens a? Leuconostoc a14
P. pentosaceus a 17 P. acidiladici a20 P. acidiladici a 21
L. lactis ssp /actis a22
I I I
J r--
P. fluorescens 06 P. pentosaceus 015
P. fluorescens 08 P. acidi/adici 019
P. f/uorescens 09 l
L. /actis ssp cremoris 010 -E. coli X3 Saline X30
L. lactis ssp cremoris •11 L. lactis ssp cremoris •12
E. coli •18
I I
L. lactis ssp cremoris •13 L. helveticus •29
Figure 8. Dendrogram produced by the Centroid Method.
B. subtilis
L. casei201
L. lactis ssp lactis
L.helveticus
L. casei202
L. casei202
P. pentosaceus
L. lactis lactis
L. casei201
L. lactis ssp lactis
P. f/uorescens
Leuconostoc
P. pentosaceus
P. acidiladici
P. acidiladici
L. lactis ssp lactis
P. fluorescens
P. pentosaceus
P. f/uorescens
P. acidiladici
P. f/uorescens
L. lactis ssp cremoris
E. coli
Saline
L. lactis ssp cremoris
L. lactis ssp cremoris
E. coli
L. lactis ssp cremoris
L. helveticus
•2 ·27 •25 •28 a4 as . a16 a23 a26 a24 a? a14 a 17 a20 a 21 a22 06 015 08 019 09 010 X3 X30 •11 •12 •18 •13 •29
I I
I I
I
I J
,, ,,
~
t--
I I ,, ,,
-
-
r-
-
l~_e" ........... 7l Figure9. Dendrogram produced by the Complete Linkage Method.
49
50
two clusters is large. The genus Pseudomonas is the most successful with three out of
four of the replicates (6, 8 and 9) being found in the same cluster. The genus Lactococcus
is the least successful with the replicates being spread out over all the clusters.
At the species level, P. pentosaceus was spread out between clusters but two out
three of the P. acidiladici replicates (20 and 21) are in one cluster with a very small
similarity distance. The third replicate ( 19) is in another cluster with a large similarity
distance with the other two replicates. The two L. helveticus replicates (28 and 29) were
not very similar at all being found at the top and bottom of the dendrogram with a very
large similarity distance. The most successfully clustered species is L. casei where three
out of four of the replicates ( 4,5 and 26) are found in one cluster with a small similarity
distance. The fourth replicate (27) is again found in another cluster with a large similarity
distance with the other three. Of the three L. casei replicates in one cluster, two ( 4 and 5)
are of the same strain, L. casei 202, and have a very small similarity distance, making a
cluster oftheir own. However, L. casei 201 have not clustered together and have a large
similarity distance.
Overall, it would be expected that each genera of bacteria would have a cluster
and within that cluster there would be clusters for each species, and then again for each
strain, with the similarity distance decreasing for genera, species and strain respectively.
It would also be expected that spectra of different replicates of the same bacterium would
be in the same cluster, and ideally next to each other on the dendrograms, indicating the
greatest similarity. However this has not been accomplished consistently even though it
has been successful to a very limited extent in a few cases.
51
Interpretation of Results
Naumann et al. (1990) achieved complete differentiation of the clusters of
different genra of bacteria. The clustering was also conclusive for most species and
strains of bacteria. This was not consistently achieved in this study. One reason for this is
the difficulty in recording spectra of a sufficient quality as in the case of Esherichia coli.
The spectra showed the multiple splitting at 1550cm-1 that can also be seen in the spectra
for Bacillus subtilis, Leuconostoc and Lactococcus lactis ssp. cremoris. This splitting
pattern was probably due to the over all low intensity recorded on the spectra rather than
being caused by a specific bacterial component. The peak pattern was also more variable
between spectra. The peak patterns in the spectra recorded from these bacteria were
probably caused by a weak concentration of the bacterial sample and had relatively little
infonnation about the specific bacteria that were used. This trouble is an indication of the
potential problems in using the method consistently. A large number of cells and the
operate skill play a crucial role in determining cellular identity.
These concerns could be overcome in part by using a minimum bacterial
concentration to record the spectra. The bacterial sample could also be used for plate
growth to ensure that there was no contamination in the sample. A minimum absorbance
could be required for analysis and at the analysis stage, the spectra could also be
normalized using the total absorbance or the strong amide peak to remove differences in
absorbance due to bacterial concentration.
The statistical analysis could also be further developed to achieve more
conclusive results . This could include using first or second derivatives of the spectra,
using different windows of the spectra, and weighting the different windows that are used
52
for the analysis. For example, Naumann et al. ( 1990) used the first derivatives of the
spectral ranges 3000-2800 cm-1, 1200- 1900 cm- 1 and 900-700 cm- 1
, with weightings
of 1.0, 3.0 and 1.0, respectively, for the classification of the genera Staphylococcus,
Streptococcus and Clostridium. But in the same paper, the second derivatives of the
spectral ranges 1200-900 cm-1 and 1500-1200 cm- 1, with weightings of2.0 and 1.0
respectively, were used for the cluster analysis of different species of Clostridium. The
paper also indicates that the parameters were systematically varied until the resulting
classification agreed, by and large, with the desired classification, according to the
principles ofthe means-ends analysis (Newell and Simon, 1972). However, this empirical
approach also limits the amount of predictive information that can be derived from a
spectrum.
In conclusion, the results presented have demonstrated that FT -IR spectroscopy
can be used to produce reproducible spectra from bacteria. The statistical analysis of
these created successful clusters for several of the species used but was inconclusive
overall in being able to distinguish between spectra at the genus, species and strain level.
This may be due to the operate skill required to grow a.sufficient number of bacterial
cells needed to produce spectra of a consistently good quality. A second factor may be
the statistical analysis required to obtain more conclusive results including nonnalizing
the spectra, the use of first and second derivatives and weighting the various spectral
windows according to their significance in distinguishing bacteria.
53
CONCLUSION
The results of this study have shown that it was difficult to produce highly
reproducible spectra for bacteria using this sampling method. This may be due to the
operate skill required to maintain consistent growth conditions in order to achieve a less
variable final concentration ofbacteria in saline solution. In future work, this could be
improved upon by using plate growth to determine the density and possible
contamination of the cells. This would confirm the bacterial concentration of the sample
used to produce spectra and could also be used to determine a minimum concentration
required for reproducible spectra. This would minimize the variability in absorbance that
is observed in the spectra.
Furthermore, the regions of the spectra used for analysis were taken from the
literature where they had proved successful (Naumann et al., 1990). However, this study
failed to conclusively distinguish between the bacteria using these windows, even though
there were some sub-samples that were successfully clustered. Future work could include
further statistical manipulation of the data including normalizing the spectra on total
absorbance or an amide peak, using first or second derivatives of the data and weighting
the different windows until a more successful cluster analysis was achieved. Other
windows of the spectra could also be used for analysis.
So overall, this study has not been able to demonstrate that FT -IR spectroscopy is
a reproducible method to identify bacteria. Spectra were successfully recorded using the
sampling technique but future work would need to include determination of the bacterial
concentration, and further statistical manipulation of the data.
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Naumann, D. , Helm, D., Labischinki, H., Giesbrecht, P., 1990. The charactetization of microorganisms by Fourier-Transfonn Infrared Spectroscopy (FT-IR). In: Nelson, W.H. (Ed.), Modern Techniques for Rapid Microbiological Analysis. VCH, New York, pp. 43-96.
Naumann, D. , Helm, D. , Labischinski, H., 1991. Microbiological characterizations by FT-IR spectroscopy. Nature. 351 , 81-83.
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56
57
APPENDICES
58
A: TENTATIVE ASSIGNMENT OF BANDS IN BACTERIAL SPECTRA
59
TENTATIVE ASSIGNMENTS OF SIGNIFICANT BANDS
IN BACTERIAL SPECTRA
Table A 1 Tentative Assignments of Significant Bands in Bacterial Spectra (Adapted from Naumann et al., The Characterization ofMicroorganisms by Fourier-Transform Infrared Spectroscopy (FT-IR). Modern· Techniques for Rapid Microbiological Analysis; Nelson, W.H., Ed.; VCH: New York, 1991.)
Relative F -I requency em Intensity Tentative Assignment 3500 m OH str of OH hydroxyl 3200 m-s NH str (amide I) of proteins. 2900-2800 m Different CH str in fatty acids 1655 s Amide I of a-helical structures 1637 s Amide I of p-pleated sheet structures 1548 s Amide II band 1515 m "Tyrosine" band 1468 w-m CH sc of CH2 methylene 1250-1220 w-m PO str of PO phosphodiesters 1200-900 m COC, CO dominated by the ring vibrations of
polysaccharides 900-600 w "fingerprint region"
Abbreviations: s strong w weak m medium v very asym asymmetrical sym symmetrical str stretch sc sctssonng
(!) (,) c ro .0 I-0 t/'J .0 <(
.3 I
. 2
.1
0
I 4000 3500
1_, •.• •
..c u ...... Q) ..... ...... ffJ
::r: 0
...... /.::.:
~ .~,.
; ,.- .-'~
3000 2500 2000 Wavenumber (cm-1)
ffJ c 0
·,:: c ..... 0 ffJ
..0 ttl Q)
"0
E <
>:-. c 0
·,:: e 0 ffJ
. ..0 ttl
:.. ::r:: ~ u ' . ; :'•.;!
' ·
'
1500
Figure Al Sample Spectra showing tentative assignment of peaks and windows used for analysis (shaded regions)
Q) ffJ "0 c ·::::: 0
... ttl ·,::
..c ttl u ..... u..o ttl · -ffJ > >-.bl)
0 c 0.. ·:::::
1000
c 0
"@) Q) !-; ...., c
·;::; e-Q) bJ) c
1.;: I I
I
0\ 0
61
APPENDIX B: FULL SET OF SPECTRA RECORDED
Spectrum 1 ~
.3 I ~~ 1\ i
j .21 1\ lfi\ ,, I ·v\ I v,, ;\
' 1-~J ' ~ V\M! ~ I "·,. }\ ( ''-J o I .. __________ _; I l---·--··-·-T-·--······-.. - -r-----···---r -----··---········· .. ···r······.. ·· ..... , ..
4000 3500 3000 2500 2000 1500 Wavenumber (an-1)
.3-Spectrum 3
I 2·
,---1000
.1J f '-,._/\ .,_J ~}\ \tw\ ._ .. ______ }
<1000 -1------r------y-----.... - '
3600 3000 2500 2000 1500 T
1000 Wavetunber (an-1)
Figure Bl. Spectra of sub-samples of Bacillus subtilis, replicate 1
.3
j .2
j .1
0·
--- --------------------·
Spectrum 2
...... )\_ _____ _
4000 r----.,-----.-·---···--··----· -·-· ...... "!' __________ ' ........ .
3000 2500 2000 1500 1000 3500 Wavenumber (an-1)
.---------------····
.3 Spectrum 4
r .1
0
4000 3000 ,-----"T----.----.-
2500 2000 1500 1000 3500 WIMII1l.l1lber (an-1)
0\ N
.3 Spectrum 1
~·~ /\~ - I - ~--~------~
O·l
.N~ ...........
1----.,--- ----r--·-·-·--·--T---···· ..... , .. ···--· ---,----, 4000 3500 3000 2500 2000 1500 1000
Wavenumber (c:m-1)
.081 ------i ·---· : '\ r- n
.06
1
1 1~ \ l \ I 1 ~ .04 \ _\ ; v I ~ I \
J
" I '\ II I liJV \ I 0 ! J ~" !I. I l \,..,- 'f\
i \. J ' 1 I 1 .J. , , ., I \.
-.02 i -- -J.,~ ' I II ' ~- I l -- ......,./
·.04 Spectrum 3
·.06
-.08-
-----r-· 4000 3500
- - , --·-··-···· -· 1 · --·····-·······-·r-···· 3000 2500 2000
1 • - -1500 1000
Wavenumber (c:m-1)
.3 Spectrum 2
II I Ji I
i\ 14 l I i I\ I 'VI . r .1
/\ 1\
I .,-"' Of. ·"'-,.!-.,./ ... \."' ! ·--- A,
--1\
. • j\ 1-
--~.J \.;_,1\j\) \~~--)_ I
i I ,.-~. - -,--
4000 3500
.06 , -
- - ·-- - r- ---3000 2500 2000 1500
Wavenumber (c:m-1)
r~ --
I 1000
1 I\ - !\ II .02 1 I \ ' V\ A
! ' , I \ ! I I ' .~J \ ~ "1 J ~ 1\ / ~v \ \J ~ ,.I All/ '·.J \ I \J\1
-~ ~
I ·- .-1 U ..... ' . I Spectrum 4
i
-.oe l L
4000 ' 3500 1
3000 ---,-- ·----r------- ,--
2500 2000 1500 Waverunber (c:m-1)
I 1000
Figure B2. Spectra of sub-samples of Lactobacillus casei 202, replicate 2
0\ w
.3· Spectrum 1
I ~ 1·21 II 1 I ,k
· 1\ IV\ ;J\\ )' .1 I , i ,.1~- ---"-.. ' ;· I ' '\'\..__ Jl' O·j .. -""'-•' ·-'"/ ------. ...} \ ............ _____.,;
4000 3500 30'oo --2~- .. ~ Waven001ber (cm-1)
.3 Spectrum 3
~ ~ .. A
1 1500
.. , 1000
- 1\
L /~ /0~ 1\ I 1 i 1\.._f\v · I ~/ "--""---- .. J~i J . ~J~j IH --'-J'. - 1...._ -- ____ )
......... r .... .. ................. r - ........ · · --- ,... ..... ---.... - ...... T_ ....... .. .... . -.. -- ------r·-.. ·--4000 3500 3000 2500 2000 1500 1000
wavenumber (cm-1)
.3 Spectrum 2
~ .2 r ,I 'P· / V
1 r,l\ ~,(c...l \/ \ .A .. ./i1
I ,I 1\ I "'",
f ~ 11
' ' I .. ___ _;
. '··-. ~ o L--~ .. J ·-- ...... _]\
-------.-----...,~~~ <¥--¥---r----~¥,.-.---~ ·---r-------·----~---··- -4000 3500 3000 2500 2000 1 ~00 1000
.3
r . 1
oJ ·-A-.,.rv-'
4000
Wavenumber (cm-1)
Spectrum 4
;\"', 1
3500 30oo
i\ I\/\ I \ \ ) .
··.. i~\ ) ~l\j~J\ . _¥, \. ... ~---- ----,. ¥.~~--- .--·'"
-----.-- ---r-2500 2000
Wavenumber (cm-1)
I 1500
r-------1000
Figure B3. Spectra of sub-samples of Lactobacillus casei 202, replicate 3
0'1 .j:::..
.3
' ~ .z j '" '
i <( .1
o i
4000
~ .2
J .1
0
..-.---./'.;T"
4000
Spectrum 1
" 'I !\
/ \. .. , -~\ / •v '"'-
/ ', /
/
3500 3000 z5ho 2ooo Wavenumber (cm-1)
Spectrum 3
/
/\ ! \
I \
3500
·--...r-, .,_
··r···· 3000
_,~/
.. T ..
2500 2000 Wavenumber (cm-1)
/
'
l\
j\! \ \ f\/ \/ \.,. /, ~ v"\}
I 1500 1000
; ' ~ (\ j '\.
'I • ~ /\
.. !'vi'./ \ ~ l \A: v ,_,._;, !}; .
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1500 1obo
.3
~ .21 ~ I
~ _, ;
0
4000
.3 i
~ .2 :
I I < _, I
r ,, I'
/ \ I
3500
Spectrum 2
~'\ ..
l" 3000
.T 2500 2000
Wavenumber (cm-1)
Spectrum 4
I t 0 ~ I \ 'v~
' / I / I
~A _ _,; ! .J"··'- ~v/
I o4000 3500
I 3000
; I, ·-· ···--·
r 2500 2000
Wavenumber (cm-1)
Figure B4. Spectra of sub-samples of Pseudomonas jluorescens AFT 29, replicate 1
;\ !: II
l t ! " \ I! r"-: , 1 1 ; \
~- \ /\ ' ~ '
~ i\// \/ \..J_J ......._,
r 1500
r 1000
f ! ' I\{\
\i\ ,, '.J \ (\ / \.
\.:-./\,./ \/ ,,.,.-. ...
1500 1000
·;
0'\ Vl
.. ,
.3 Spectrum 1
j .2 j
.11 1l <{
~ II I\ ~~
A • I )1 r '\ I · i ( \ I \ \J 1 ~
I \ II I ' h) \ /' . ' \_ wv \; .,...,-0/ I '"'"' I
L.>-,)\.,/ '\."' ..... _ . }i_ . --- __ j
~--·-·r·.. 1· .. ,.. ' ········--,------r .. ···-····· .. 4000 3500 3000 2500 2000 1500 1000
Wavenumber (cm-1)
r·· -I
.3 Spectrum 3
fl § .2
1l
li . {\ r ' I\ 'I. I
/ \ i i 11 / \ I i \ I I \ I · 11\ Ol I ~V"\ If /'1 ,\
_.,,J '\~ _j\ I wJ 'vA,.I, ... -----~-" I I
~ <{ .1
1500 10";0 - --. --;-~------ ·- -----~----- -
3500 3000 I ... ., ... -_ ...
2500 2000 4000
Wavenumber (cm-1)
I I
. .31 Spectrum 2
I ~ .2, ~ I ~ .11 /\
i I I \
I \
i ' \ o 1
I ,_ ! I V'\ i ; \ f-~~,.._/ ,,_ I '-.
I· ,•\ ···--.! \,. .. , .....
/1 II ,, 'I I 1 t1
I VL i !\ . I ht 'v v \~;•,_;1:' I '
--~j i···
4000 ......... r··
3500 ···--·- r···· -,--~ ------···-~--1--·----·-····
3000 2500 2000 1500 Wavenumber (cm-1)
.3 Spectrum 4
j l
j ,,,! I
+ .......... ,
! . 4000
........ -- ~ -· ·r--~-----------· -----,·- .. 3500 3000 2500 2000
Wavenumber (an-1)
ji H {r
' I
1500
1000
-r -1000
Figure BS. Spectra of sub-samples of Pseudomonas jluorescens AFT 29, replicate 2
0\ 0\
.3-:
~ .2-l
I /\ ! \
.1 ! \ // \ I \ I
I 0 ' f
~ -~-v...fv-" /
4000 300o
Spectrum 1
'rl v\ ~~~ -. !'' ' \
.. , 3000
r .. 1 2500 2000
Wavenumber (cm-1)
i __J
T-· 1500
f~
/ \ ! \\, ' ,, h
...,, ·vi l
1000
: .3 i
I Spectrum 3
r1 .1 1 ,'\
, I ·.
I /\ o] j v\ __ I _,--.....1 .... ~
:···· ·-· I ··r 4000 3500 3000
;~ 1 l
1: rl
h ;1 1
q'i I 1 h r'' i If I r 'I
i"\ )\/\ / ~J' \I \ ...
j
--.-------r--··- _____ "'1 ___ _
2500 2000 1500 1000 Wavenumber (cm-1)
1i 'I
~~~ /i
i :
.3
! Spectrum 2
rj < .H
0
... .. --,,.,. /'\
/ i--"'~f· ... ,u<'"
"Joo 3soo r........ ... .................... T....... · ............ ,. ... -
3000 2500 2000
i .3!
i .2 ;
< 1 I I
Wavenumber (cm-1)
Spectrum 4
;\
j\.\ I '../"\
!l
~ !I I'
;j 11 i! ii
I \ ~ ' . i\ ,, I \; I .. 1\ I \
) \..j./ .; "' '
--· r···
1500 1000
I I
I I ,, I ,\ I li il 1
I\ II ,., :
1·\1\_i\/\ I
l f'-/ v \ .... A_ ,-. ;
I I
,/ '\ ..... I \'>; "\j
_j \:
4000 1
3500 3000 r ............ T ..
2500 2000 Wavenumber (cm-1)
1500 J
1000
Figure B6. Spectra of sub-samples of Pseudomonas jluorescens B52, replicate 1
0\ -...)
Spectrum 1
s J .1 -j
I
o4 I
i \ I '
I \ / \,"-.t'\
! \, /
"'-"'"' 4000 3500 3000 2soo 2o0o
I .3 1
r .1
Wavenumber (cm·1)
Spectrum 3
I\
1500
I .
\ \,; ..... ~ ....
r 1000
!~
\ 1\ j
I vI /' I ~ . i..Jv"v ~j } \ _______ )
0·
I i I \V\, ___ _,.__,_,__,/ '-......
- --,---·-·-·· 4000 3500
l ' . ....... .. . , ....... ['
3000 2500 2000 Wavenumber (cm-1)
'[ ... 1500
•. 1" ··-~·----
100o
.3
i .2 ,
:l _,_
0 · I ,,.~.,..,_,r,__,,i'
Spectrum 2
i\ ! \
; \ I \
'\\J"\
----"·r· ------~-~
t. i'\
.i \ " ---
' ~ il il ! ' i -~ \{\ \} \ t\ v 1 /'i / \
~,j\/ \! ~\ ·\j '
4000 3500 3000 2s00 2000 I
1500 1000 Wavenumber (cm-1)
.3-Spectrum 4
n .1
l._J 11 . I
/\,\ \ )! V ~J~A .. ~ ~~ ~ \ ' ··--·--.. .. _____ j·L-- --~--;1' I
1-----,. ----.woo 3!500 3000 2500 2000
... , .. . _ .. ---,---· 1!500 1000
w._,umber (c:m-1)
Figure B7. Spectra of sub-samples of Pseudomonas jluorescens B52, replicate 2
0\ 00
.31 Spectrum 1
~ .2-
I .1
0-i __ ,~J'-...,.. ...... .,. .......... /~,.-, __ _
L-4000 3500
.3
A I J~\ f V\ /'-.,/' .. A, '\j' '--. -· \ . v-- .,_ , !
vv . - -- --- - ·-------'"""" I 1- -----.-------.----
3000 2500 2000 Wavenumber (c:m-1)
Spectrum 3
---.-----·r·-1500 1000
' __ J
r .1
' t\ -"'-.., 1·1, I v\ ;J
0 ! . ~"\)\,...,. _ .. ...-.---.-- '-- .. "'v-r ·----------I \ J · \'V'·-· r•._/'~-~---'---~~~~~-...,.,..
j-----,-------,--------------,------------------------·r----. ---,-4000 3500 3000 2500 2000 1500 1 000
Wavenumber (cm-1)
r .3 I
J .2l ) .14
I
Spectrum 2
t\ 1 'In ,./''·.,_ ~
/ '-~ I o I ~-"-. ./l....w_..-- '.rv- - -- ·---.! .. _ ; 'v ~v-~/'-~,\
40oo 3500 .............. ""T' '
3000
,---.3 i
I
r .1 ·
//,.,,/ ........ ,, ___ ............
- ~~-- vv
l .. I .................... r---·-- ... 4000 3500 3000
.... 1... I
2500 2000 Wavenumber (cm-1)
Spectrum 4
I '\
-, 1500 1000
~~ / V\ -- '~ J i ,_. . -- - ----. __ _,.,. v ·--"--- '---~- ---~~N
!'"'"'"""'"""'"'"" '"'""''"!" '
2500 2000 Wavenumber(cm-1)
r 1500 10oo
Figure BS. Spectra of sub-samples of Lactococcus lactis ssp. cremoris , replicate 1
0'\ 1.0
.3· Spectrum 1
~ .2
--~-· --.r..-~""
j A\!': __ II
i .1
./·-, -~. -------·"-, __ ---.... _____ _j\ I \..~ , __ ,,j "--- -- ----" i -~-----..:
0
L 4000
--.,--- .... --- .............. .,. .. · ---- ----·--··---··r -------- ................... T .
3500 3000 2500 2000 Wavenumber (an-1)
,-1500 ' 1000
.3- Spectrum 2
r .1 ,,
_/ -,_ ' I 1\)1' I . ·--,~-·- I \. ;· ___ __.._, ,.,_______ '·-- ... ---- 1,\ ; '""-- ·' \./''·· -·
--- .... ~ ... } - · -·¥-- -- ~~------' '"-~ ,,_
0
1 ...... ----------,------ ---------,----------------------- r------ ..... 1 ---~·--,-·---- -- --,---------4000 3500 3000 2500 2000 1500 1000
Wavenumber (an-1)
Figure B9. Spectra of sub-samples of Lactococcus lactis ssp cremoris, replicate 2
-..)
0
.3
/'
i .2·1''·"- // .... l ~·'
< .1
0 · i
4000
.3
--,--· 3500
f\ I I I· l v,
··, , I;; J t ,_ .. ,, . /.-, " ~- , r' , ~, •· , ..• ·-. ........... ,_ , \.. ( ·--,.""' ',, ·-----~ \
Spectrum 1
1 ' r 3000 2!100 2000
Wavenumber (cm-1) 1500
I
1000
'¥
1.2j .1. t\
/-·- '· .. , 'Y\ i v~ . -·""-t¥.,.J 'vv-_, j I lr ...,r-.-,f-\, '""\ , • .. ,., _ _ , ~ ' I
1 -...... ~-----,-~,)0 "'~ . \ 0
Spectrum 3
r· ~~. --r- ·····-·· .. 1 ... ---- ·· ·············-···T-· ··················------·r ··· "'""""!
4000 3500 3000 2500 2000 1500 1000 Wavenumber (an-1)
.3
~ .2
j .1
/''··,, /' '
·-"-'A.to.~-·'/ 1\\
I . '·;.." ~~ { l r-·'v -·\ I ~ •• ~-- .... j' \ ··-- ··--~.-~J ..... . ''"''
Spectrum 2 0
<1000 3500 ··-r ·····-~ -·-· ... ----··r··· · ~· -,--3000 2500 2000
- r-- -· 1500 1000
Wavenumber (cm-1)
.3··
Spectrum 4
L . I I ! , I f J I / "., ~ . ,~ ' !"'_,__./ ~- ... J\ __ _j ~\r ""'~._! ( __ . - , ...... ---, ·----r- ...... T ........ _ ... _.,
4000 3500 3000 2500 2000 1500 1000 Wavooumber (cm-1)
Figure Bl 0. Spectra of sub-samples of Lactococcus lac tis ssp. cremoris, replicate 3
-...) ,_.
.3
~
i 0
o4000
.3
/•
~ ....•. /·
3500
Spectrum 1
''vv --- ...
;\ IV\ , .. 1'\ 1 v/'./ ' , ~~... ,
• ............ _ _.-f l \.}i
"7'~-
3000 2500 2000 1500 1000 Wavenumber (an-1)
Spectrum 3
1.2 i\
1\ jl
, ~'" I \A .1· . .... ~.// \\___ I v \
• '·-.., J v..;'\. _/\/\
''-·-"'\._ I ' '· J
J. .... ·--~--------~-v...._j' "' \"--,..1\
'L---r--r--------,-...... _.. .. ,-·----,-~· 4000 3500 3000 2500 2000 1500 1000 · Wavenumber (an-1)
.3
Spectrum 2
r .1
!',·., ~\/\ / '\,\.. I I \' ,"--A . ./\ J'
0·{ ----..~~·~~ ··-.. , tl\ J ·~ ....... ~ ... .../ - .. '·-·· ··-- .... ~J
; ----------~ ............. r------------~----·r··- ···· .......... r ....... . ... , __ _j 4000 3500 3000 2500 2000
I 1500 1o0o
Wavenumber (c;m-1)
.3 Spectrum 4
r .1
I
/ \ 1'\ l l ' IV' , / '--.... 1 • \ )I / \. I . . ! _ ......,J,,,""" '\- ·-.. ____ J'-- i l·.r--J\j \ __ ____ ..,I o I ·-...... ..• ,_! I
l -,--- -.-------r-
4000 3500 3000 2500 2000 1500 1000 - J
Wavenwnber (cm-1)
Figure Bll. Spectra of sub-samples of Lactococcus lactis ssp. cremoris, replicate 4
-..} N
.3 · Spectrum 1
I j I
/\ n I I · 'i 1 .,\ I: r . . . ~- I I ~ I ti\ f\
§ 2j
j _,/ / ""· I J \ " 1 \ 1, I - "·· '· .- ~--. . .. i ~..['-_; v \"' ./V~
"I ~-- - -._.~ I I I r- .
4000 3500 3000 2500 2000 1500 1000
.3
~ .21 /\
J ' I . I \
I \ J .11 I \ ··"--...i\1/ \.;\
0
i--4000
~--1'
3500 ·----······r··
3000
Wavenumber (cm-1)
Spectrum 3
............. 1
)t\ . \.. ____ ,. . ./
2s00 2000 Wavenumber (cm-1)
·········.-·····-··--·-·-··
1500
' \ ' "\,-... • I \.
i 1000
v,
Figure B12. Spectra of sub-samples of Leuconostoc, replicate 1
~ .2
/I . \ I '
I \
J ' \ I \ .1"j ! \.r, , __ .,;.. _ __ } ... .....,.•/ '·.,,
0·•
,...._ -~----·- ·-r-· 4000 3500 3000
Spectrum 2
.... ~ .,_ ... .,...,...-''
-·--,·-·-- ··-·----·:--· 2500 2000
Wavenumber {cm-1)
' i
• h .,
I J 1 " Iii f\
J I v • • I '11., .. ./\/ \ ;" v i
"'-'·'
---,-
lsOO 10oo
.3 i Spectrum 4
J /\ I \
i / \ 0 1¥ .A"\f ..... _,.,-' 'v\ \
-T
4000 3500 3000
I
I• 'i
i\ \ f'.
! .. ii'i
! 1(\ / \ /' II V L r ~/'v '-"~·-·
h
j \ .. ___ )
., 2500 2000
Wavenumber (cm-1)
·r 1500 1000
I
I
-..] w
.3 Spectrum 1
~ .2
j .1
Ol /\
'\ i I ,. r\ l I \J\ I \\ ;, . -~ !
.. ~ ......... _;.,.,.N'"'"/ •,,,....,.'\. "\,.
,,, J '~---
. \ '\, - , . I I · ~...r·-/" \ I ' _, i
4000 3500
I] I 1\ l I \
- r--· ·---. -- --, . 3000 2500 2000
Wavenumber (cm-1)
Spectrum 3
; 1500
I
1000
oJ / \. . / "'--!-- · ·'V-./ '"'--' ..
f1~ r\ . ; vI /\' \._,,_ I\ t ~ \N"-- " \)1 ~\ .I
.. .... j \.._. _____ ------t i I.
4000 3500 ~ --~
3000 --·r··-· ·- r-·
2500 2000 Wavenumber (an-1)
r ... 1500 1tioo
' ' i
.31
~ .2
i .1
i I
I \
I /\
' ' o I ·--.....,\ \ ... f--~-v-./
I ~ 3500 3000
Spectrum 2
- _;"-\ --
2500 Wavenumber (cm-1)
I l\ f\ i \ 1\ ;"""\ I . .t
/ V \ ,.___!\/ u\. A I , ._, \
J \
1000
'I Spectrum 4
~ .2-1
j .1 /\ I \
01 / \. ___ ,.A..(\_ / \..,, )..' ,.'J< ...... I ~,
~ i\
/'\ I!
i \ f\ j'\ ~ I \} i ,. I '\' ) I hI '1_ a:'-j\ I I
.J \ y; " \ .... - ...... --~ .__,.
I 4000
····· - ~·y ··---· .. •···· --r----- ·······T··· -~---·r· ··--··-.. -·-·· r ··--- -· 1---·
3500 3000 2500 2000 1500 1000 Wavenumber (cm-1)
Figure B13. Spectra of sub-samples of Pediococcus pentosaceus, replicate 1
-....l ~
.3
~ .. 2
j <( .1 /'
I \
I \ I '
I ' / \ Q I ,, / V''
_.;......,_;'\..,v/ \.\
--- -- - --1 Spectrum 1 I
!
1\ ' 1\ I i \ i\ f\ ! I \f' I \ 1, ! 1
\l/'-. J \i .... ,...., ,"' . I :-•
; r:~
I u 1 f\ ' \ ) i ·- ______ ., I ·- '· I \
l.. 4000
.3
g .2 ;
j < -H
- r --3500
j\
.... T - ................................... , ... .
3000 2500 2000 Wavenumber (cm-1)
Spectrum 3
o! I\ l ! 'v--
____ .-.. h./1 '-'\. .. ,;\
I r-··--· -----.-
4000 3500 3000 2500 2000 Wavenumber {cm-1)
;-~00 1000
J\f \ _ _ll
-r-· 1500 1o00
.3 Spectrum 2
~ .2
I /\
/ \
l II \
0 ' ' -,...._J...._/ ., -~\-I V•
4000
·--
1
I 3500 3000
II /i I' i A I"\ II' i \ I. i• I /\; t .\ ,, ~~ ! . I '-·•·' ''""-,J \ .J w~- " '~'
--·-- ~_/
........ ·--r···-·· r
2500 2000 Wavenumber (cm-1)
,--1500
-., 1000
.3-! Spectrum 4
I g 21
j I t ·I '\ li
<( -1 , I I 1 ~
I I I\,, •I I I \ I I /l I \ I I , \1 \ · ' 1
, -..., v r1j \ . ' ;' '" , I \,f'.J " ·---' / O·i , '\ i~ ! ,,
r'·'""'-''c// ., .. ;''l ,/
,_ \. -~---- _...__
J · --- ···r··-·--····· -. -------------, 1 -- - r-------· 4000 3500 3000 2500 2000 1500 1000
Wavenumber (cm-1)
Figure B14. Spectra of sub-samples of Pediococcus pentosaceus, replicate 2
-....) VI
,-.3 ·
r .1 i f\
I \
/ \ : "
Spectrum 1
/. ,, ji f\ , i !(\ i \} \ f\ 'I I \ .~ ./ \ /J l yJ\ .j v \ _,JJ
- -v'/ ' ---·· / ''-'"'
0 ·· ·'-.AM/ \1 /;,, 1'\
.... , .. I \_
4000
.3
8 .2
~ ~ .1
' r-·---:-- ~ ----------····-3500 3000 2~00 2000
/\ ! \
I -
Wavenumber (cm-1)
Spectrum 3
!\ n
-··--· ·-·-r--1500 1000
1
I I
1\ /;
IV\ [\ I .. I' I I ~{\---' v \_ A ' . • J ~'---"\ji I\'• )
. .} \.. ______ ------
I '"c., 0 t ... _..Jo."'(\...NI'/ \.. ........ ' --
····--,-···--·-· ··¥·¥---r···-···- ··· -· · -·r··- ·-------3000 2500 2000
·---T···----3500 1500 1000 4000
Wavenumber (cm-1)
.3-'
l'l .2 i ~ ' g ' .Q :
< .1 f '\ I\
I \., I ,........_
: I 'I 0 i---""1'•-"''
!
i · ·---.....---·-- ···--·-r-4000 3500 3000
I
I .3,
I ~ .2-i
i .1 1 /\ I I '-,
I V\ I L\,, l /
Ot ~~\.,·.<
I I
Spectrum 2
,, 1\ II
.,
I i
I v~1 /\ J /\ ,. \ .
tr, I ' · .... ,.._; \ __ ""' ......... .
1 vf'--- ,_ . .i\___.1 1 j v __ )
l ' I 2500 2000
Wavenumber (cm-1)
Spectrum 4
1500 1000
t\ )\
I if\ ., /\ ! ,, (\.J v \ I "' I
-- ____ ./
r----. -----.--.---...,. .... 4000 3500 3000 2500 2000
-·r 1500
~_I 1000
Wavenumber (an-1)
Figure BlS. Spectra of sub-samples of Pediococcus pentosaceus, replicate 3
-..) 0\
Spectrum 1 J
r·l o j /\ I · jg ' ~ J ,, ;
< .1 1 ""'-!\,_·~-/ _ 1 1\ 1 I -,_ II\ l A I "-,______ I i I \) 1
\ I .... I • ~ I I ., _____ , (.._ I t. ~j '\ '· •
0 . ---- ----~1 'V ~· ' ·~1
I 1 1--.............. , .... ---, --···-- T .... --. · -- • r- -,-.. - ---4000 3500 3000 2500 2000 1500 1 000
Wavenumber (cm-1)
Spectrum 3
g .2
i < .1
0
4000
,>'"
.. , 3500
.,_
3000 2soo
l ri{
I 2000
Wavenumber (cm-1)
...... .....
I ti j ~ l
il;
-! \
..... -.,_..-.....
1 1500
i\ ' ... ,.
1000
Figure B16 Spectra of sub-samples of Escherichia coli
i ~ j~
r
I ~ I
I Spectrum 2
~ 21
I ! <( ~ 1
0 iii:''-,.
I\(\ j "". -.,-~--~ \-......... ~.:/..,
-~
1500 ,.
1000 30~0 -r· -f
2500 2000 Wavenumber (cm-1)
4000
·I Spectrum 4
8 .2-j
f j' ! /' I < 1 \ (I . '"•"'- / "' I!
'-"v., .. / •. ,___ ~ t ·v~ .,_ '\ (, ... _ II I . \ ··- .. . ' 1\ !\ o, . '---..; \,__ .1 ~ ,,j J \,,._;"" ,. i
: ~---- ··-~ '-1 --,'
! ~ I .I ~ ·-- . ..... T. r- ··-·····-T··-··~·-··n~-~---~···-·~ ·- ....... i ·--- ~--r- .. ·· ·-
4000 3500 3000 2500 2000 1500 1 000 Wavenumber (cm-1)
-..J -..J
.3 : Spectrum 1
L 0
4000
~
/
3500 1'
3000
'\,..r'~··-
.,. __ '",. r ~ .... ..,..,__. --r j 2500 2000 1500 1000
Wavenumber (cm-1)
-,
Spectrum 3
j .1 t\ /1 ' , /~ tV\ / ol "-A.,._, ;. I v~-1\/ \ __ A.'
t-..-.A>-..~· ~ .... . .,..,. j'\ . ! ....... ' ' . "----··-"" \.1
L u . - t ;· ·T·· ..................... ,..... r _ _j
4000 3500 3000 2500 2000 1500 1000 Wavenumber (cm-1)
.3
~ .2
I .1
.... ,... ... f'....Jv,.>"
~000 r
3500
Figure B17 Spectra of sub-samples of Pediococcus acidiladici, replicate 1
Spectrum 2
~~"\.<v---"
· t ~~~-----·· --·-·--.. ---- r-· 3000 2500 2000
Wavenumber (cm-1)
,, i , r·· . j V \ J'v/\... i ~ !'......... ... J ""·1
'T'"' .-.... 1500 1000
-....) 00
Spectrum 1
i ~ .1
1\
. \.'v~ / ~:\' ............. ~ ..-:"'""
I 2500 2000
T"
1500 3s0o 3000 <4000 1000 Wavenumber (cm-1)
.3 Spectrum 3
g -2r r I j I ,\ ~
I It .. i 'II i! i i 1\ " .1! ;'\ 1 i_!\ .\ if
' I I 1 J )l'
I / \ . (V: /'1\ I
II '·v-, ~~ I ~'./'· v' \~_./' I
01 , -'c~"'-·, n J I -~,..,/ •,..._J Lc.•«.·-···-·-""
' J' L f r ·• ·--, · -- · ,-----· ---· --- -,-------4000 3500 3000 2500 2000 1500 1000
Wavenumber (em-1)
r· I .31
I ~ .21
Spectrum 2
j .1 /\\ .. "\.,,
'-'; 7 ·-•• ,, f_,h,J\.-r#'
0 1
3500 ' <4000 3000 , .. .3 1
i ~ 2 I
I I < .1 j f\
t
/ ,, / '-c ..
0 / ".,A . ~--· ... \/\./,if' \..'\..,~~ l I
i ___ .,_.,/
2500 2000 Wavenumber (cm-1)
Spectrum 4
l
4000 I
3500 ·~:·~~~~····~~--- ----~---····· ~· .... 1~ ....... .
3000 2500 2000 Wavenumber (cm-1)
Figure B18 Spectra of sub-samples of Pediococcus acidiladici, replicate 2
1soo
1500
1o00
··r 1000
\~~~1 n >,..;\
'
<~ • .i
-..) 1.0
,---1 ·--__ ,,...,.._,.:--~•n . .....,;
.3-
Spectrum 1
~ .2-
LJ /\ t ~\ ~ )v~-~/
o-L.,....JI..N"' ---···--...... . .._.J ~-------·- ""
·---·-r----,-----. - r · --,---------,-- -4000 3500 3000 2500 2000 1500 1000
Wavenumbel (cm-1 )
.3 Spectrum 3
'\ v
.3-
~ .2
i ~ .1 /\
/ \ / "•"-'
oL__....'"''·""/ \-~-
) 4000
I i
.3--i
3500 I
3000
'j .2-
'\ .1J I "'-,
Spectrum 2
.I
p\
ill j q ,\ /\ ! \j I 1\ \ .
I \ ''-.! V '•, J\ .. -'j t 1 ,:..; \)
·- .. ~--·-/
' ' 2500 2000 Wavenumber (an-1)
Spectrum 4
1500
I. !\ I i ;\
1000
V l 1 I !I
I J . )\ ·~/~ ~ ~ --- '-----~,./
l / "\_, / ........
~o... {\,,vr·· ., o-J _,.,. ,. J ~/J\/ \
----..... ~.-- .. _,.,.._, •• .!
I , ----- 1 4000 3500 3000 2500 2000 1500 1000 4000
Wavenumber (an-1)
Figure B19 Spectra of sub-samples of Pediococcus acidiladici, replicate 3
3500 3000 ·-·---~ .. r · ·-·--------..... -.... ·r·-·-·-·"'
2500 2000 Wavenumber (cm-1)
I 1500 1000
00 0
.3
Spectrum 1
~ .2
i < .1
{\ II '
0 -1
f V\ , !\ . ' l \..t ../ v "--" '-'\) J
- ~-""~
,... ----,------...,.......-------. .......,...----4000 3soo aooo 2soo 2000 1 sao 1000
Wavenumber (cm-1)
,---· .31 Spectrum 3
i
j .2 j
I ~ ~ , i ' jl ! ~~ I I /' . ....... I I A, , , J
I ..?'"" ' ., J'\ f ,,/'" j ''--<-" ./ 0 ~~ ,,.,v-.. ~--- __ _ _ _ ~'
-~- -- • r · ---,----T-4000 3500 3000 2500 2000 1500 1000
Wavenumber (cm-1)
.a
~ .2
j .1
0
- -'"'.! _ _,~/ /''
//" "" """'-' \ ''\t.-..,...,
Spectrum 2
\
I\ '" 1:~) i v \ ' . ' I f
• ._, J '"--- ~- ) ·-~""' .t\/ \ J _A, ~,.-,., ',.~,.-,_-,/
-r-- -------,---- ..,...--· ----. __ j 4000 3500 3000 2500 2000 1500 1000
Wavenumber (cm-1)
.3 Spectrum 4
~ .2
j .1·
0 ; v . ~A.-..JV.. ... _ __. - .. - ·--... ,., -· ----. J{l, / \ ~"""··--'"~'--/\, v v .... ~--. \ _ ----~-~~> v - .......,_;...
\ 40oo
r-~-----· ....... ...,--300o 3000 2s'oo 200o 1 sao 1000
Wavenumber (cm-1)
Figure B20 Spectra of sub-samples of Lactococcus lac tis ssp /act is, replicate 1
00 ........
I .3, f1
I\ Spectrum 1 l
,, /I A
( I I i 1\ I \ \/1 I
g .2 j
~ I .:g < .1·· I \ I VI /\ J
1 \_v-\ J ~~j\)\J \ JJ/ . i '\ I ,y·"
o l I ', ~· ! ·
l~j c ~ '~'!_\= .~ -~ -~J 4000 3500 3000 2500 2000 1500 1 000
r ----
I .31 ! I
Wavenumber (cm-1)
Spectrum 3 r ,I ;j
il , I ~ l\ I. II I \ i Iii I ' . \'I
~ .2-i
I . i \ I J I [i 1 1 ;' \ ! i .~J \ .. , , , I
. , I '\J\ I ~lj\,; \ \ .v-'
) \ ) -'\.'
~ ~, & ; .......... ! i . - .• .J \ ____ . ·--- ---
1=-= ··r·-- · r ··--r---- r -- ·-,- ----4000 3500 3000 2500 2000 1500 1000
Wavenumber (cm-1)
.3
~ .2
I ol
i I
4000
.3
r:
-~.f"l
Spectrum 2
1 ..•. 3500 3000 2500 2000
t j\
I \ i \ I \ ! \,
I \t1 .' \
Wavenumber (cm-1}
Spectrum 4
_)
n
ll 1-i
1500
~ ii ll rl, ' I i I \II
i 1000
., l~,
\/I A t\ ~fvl V \JL. /'\.;
0 I \ I ~
_,....,,,._/ "'-... 1.. ~-- }\
4000 -........ -r---..--r-,.·-~-- ... ~·-·--R,.~,~-----.. ------......... ,. .. r·---~--~---···-r-------
3500 3000 2500 2000 1500 -------~-~---
1o0o Wavenumber (cm-1)
Figure B21 Spectra of sub-samples of Lactococcus lactis ssp. lactis, replicate 2
00 N
.3 !
I ~ .2-1 ~ i ~ .1 I
I 0 1
' l I
4000
~ .2
j .1
4000
J
I~ i \ I \ I \
/
3500
Spectrum 1
I 3000 2500 2000
Wavenumber (cm-1)
Spectrum 3
~i _It__
3500 ""'"'! .... r ---·........,.-·
3000 2500 2000 Wavenumber (cm-1)
1500 1000
1
'!
--
\ ! fl Ill . \f I /\ 9! \ f\ ' I I ' i f1 \/ \\ r ( w •/ ! --/' l
1 1500
l
t t
~ 1000
.3 Spectrum 2
j j .1 !\
/ \
/ \ 1 ' 1\ l .< (t\ j l
!
I 11
1.};
/ \;\ i,.,j'v} V 'c.v-- / ! 0 ~~
j
"'J,_,_f(..,//) ~ / ~-
~ r~
4000 3500 Jdoo 2500 2000 1500 1000
I
I I
.3 ! I I I I .2l
.1
Wavenumber (cm-1)
Spectrum 4 t! J\
1 l I
r \ fl r r\ / , IJ\ I\ I, !'
' l_ {\ ' \
"" 1• vA- v \ .~. " 0
I ~ -~\ I " J .
I \,' i~ ) / -.-...... ... .._ . .,_...,j \.._ ._.,.,. .. _.. ... -.n·--• •.J-.Jo/
- --.,~·---,~~-~~ 2500 2000 ' 3500 30bo <400o 1500 1000
Wavenumber (cm-1)
Figure B22 Spectra of sub-samples of Lactococcus lactis ssp lactis , replicate 3
00 lJ.J
-··----~-------------·- ··-------.. ----.. -·--rr--·--..
J ·l Spectrum 1 I ~ I I \ I II j ·~ \ I f\ I I j I\;\ 1~\Pjl)
< .1 · I \ ) ~" I '."-, '
~J<w.}>./ ""'JL. __ ~_., 0·1
------,-----··r-- - ·--,-----r-----,-----,---4000 3500 3000 2500 2000 1500 1000
Wavenumber (an-1)
I .3 l Spectrum 3
I --l ,, ~
r .1·
~ · '-.... . .~~ ._j '\.,_ ___ _
J f----.... ~-----1---------y·------,---· 3500 3000 2500 2000 1500 1000 4000
Wavenumber (an-1)
~--
.3·11 Spectrum 2
-,r·--··-
II '\ I VI
-----
'\
I' /\ j i I \J" <'I J \ I
o-1-~......,)'1.,. '"·~tL __ . __ j I
L ~ !r\\ ~JU\t v \-.,A~/\
1--,----r- ------"'r"""' 4000 3500 3000 2500 2000 1500 1000
Wavenumber (an-1)
~ -· ·rr ,, It I \ Spectrum 4 1 li\
I \ I VI .2] I \ h 1 1\ ~ ~ A
j I v \\ I ,iJU v \ ' II
J J \~ ' ~j ""~--~ 0-1
.3
I 1-.. ------T ..... ----.. ·r ·......,-------,
4000 3500 3000 2500 2000 1500 1000 Wavenumber (cm-1)
Figure B23 Spectra of sub-samples of Lactococcus lactis ssp. lactis, replicate 4
00 ~
.25 ; Spectrum 1
.2 i
.15 I ' ~ l ' \ I \ I .1 ! J ...
I . I \"\ i \i\ .05 j
f. ; 0 · ! . . !, ~?) II\
'\
-.05
' ,.
4000 3500 3000 2500 2000 Wavenumber (cm-1)
25 ! . I
1 .2 ! Spectrum 3
] 1\ .15 i ! \
1 I \ i .1 : I \ ! I y,
<( .05 j j \.~ ' I -~ ! I
Oj / t--""'"-.)\. .•
I\\ ~,_" \ .....
-.05 j
1 n
~~ jl 1
ll t ' \! \ {': i ~ I )
I ! '' ,. i r ' 'v' I \ i W'\)\J -~~ ! .... ..,.1\J
f '1
\ 1500 1000
I l fi 'I, t 'l
ll\ -\ ., I ' vI ,\ 1 1 i
\I fl,) v \)--J\ I i\: v
····--·~--r_._.__.·- ~--- ·-y--- -r--- - r 1500
I 1000 4000 3500 3000 2500 2000
Wavenumber (cm--1)
I
~ .2
j <( .1
.15 i
l Spectrum 2 .1 ..i
' ! i
.os..: ' i i
0 !
!' ! \
/\ i \ ' "' / \l')
I 'I
i I J... l / "·,,
-.05 -~- dJ-"),}vf
I t
l 4000 3500 3000
~ ' I 'I l i . I
·-." \_ ---· ...... _J
2500 2000 Wavenumber (cm-1)
Spectrum 4
<\
t \.
I ~ II ! l i ! h l ! !\
• I
1500
' \, \; ., l h '
r 1000
l
I ; ol-- --'-..:v·
"- \
~ i /'\ 1 \ rV\.~' '.J \.,.J' ....
r ,.,
4000 3s'oo 3000
{11
\. -- _./
2'500 -- 2~0 Wavenumber (cm-1)
'"l'" 1500 1000
Figure B24 Spectra of sub-samples of Lactobacillus casei 201, replicate 1
00 Vl
r~-
~ Spectrum 1 .3
! l
~ .2 i
I ~ 1 ' • . ··t ~ j\
!.\ i \ Vv\_1 \;
0 f ""' '"'<;.
l 4000 1500 1000
! "' r-·· 3500
r 3000 2500 2000
Wavenumber (cm-1)
0 n, i \ t II' ! 1 1 Spectrum 3 I 11\
.2 .
.1 5~: I \ 1\11 i I . d f\ r I . \ I 1 ' I I .1 I I ' ! f\ i 1
j I • \ ' I ~;I i I \A I ~ J \ I I
.o5-i j v \ J1~ 1J W V ~ \ 1
1 f1 I ~ ! I \ :1\ 1,.; -- \I
o; I ~ i' I 1' r ... ~tV "'-· _j \ . --· --~J 1
·.05·1
j i ; ; ;·'·
4000 ["' ~--~---.----'1-''
3500 3000 2500 2000 Wavenumber (cm-1)
' __ ..J
1s'oo 1000
.2
•\ 1\ I I I \
I I \ 15 .. 1 I
. I I \ I ~ , "' j .1-j 1 \J \
Spectrum 2
. I ' I I ~ ~ .051 I ~I Ill, ; 1 -........,} \ 0 J,. _,.~<-.\}1.1
· .05
... ,,. __ r-3500 4000
r 3000
I 1· ·-·
2500 2000 Wavenumber (cm-1)
{1 i\ ) \
; I! ! l I I'' 1 ! \ • \I 1 , \ #I j ·, I
0 '\1 \; v \ ~ v, 'vi-A
I" 1500 1000
r\ r , / \ ! ! j1
.2
.15 j \ Spectrum 4 . ! 1 !\
I \ ! \) i i \ I lll ,\, 1'' I I \ I 1\ I
!11 •• I I ' \ ,, I I 'I i!! • 'I ' . /' I
! \ I lnt ) I I \ ; i I
, \)f\ J \ ' \ .r I ~ .05- I ' I Vi'\_, .; \ j I ~ .I \, J ~,
o l ··""v~) -"-· .. 1~1 1 I "'· \....~ .. _J i I
-.o5 ! I i
"dOo "}
3500 !'
3000 I I
2500 2000 Wavenumber (cm-1)
I I I I i i
r----~~¥··-~~~~¥- ---·-r ·-· ·-·¥¥---....J 1500 1000
Figure B25 Spectra of sub-samples of Lactobacillus casei 201, replicate 2 00 0\
r----~·------........,.. ----------_,...,...;.,:,.. .... ...,.... --~-- ·" -- r· \
·--···-- ...... , i .3
Spectrum 1
B .2
l\ I \ I ,
. \ I \
1 \ r I It\ I' v I 11
c:
I I ' / i <( .1 I \ I ~JV\.~
I I
I r- ~., _ _,.)
' \
o I _,_..-vr>./ \, ~-. P\ .. I •
4000 3500 3000 r !
2500 2000 Wavenumber (cm-1)
I .3 -1
T ......... . 1500
~~---·
I l \I
.. 1000
l B .2·
('\ Spectrum 3 ·'II\ I \ I I ~
I \ ' I '~J~~Jl' I \ j~ I " I j , J
0-l .. rA.,J) "'...._,, l ... -.. ...... I I .1··
i ................. 'T"" T ...... --- ~ -- ·~··r-·-r-· j 4000 3500 3000 2500 2000 1500 1000
Wavenumber (cm-1)
.3
~------------n--···-- ....... ---~·-· --~
I \ I I
I \ I \
Spectrum 2
~ .2 I \\ ~~ I \i \. I ' v
I I I I
! \ I ' ' " I \,,
Ol I ~ /i -·"-..t..! "--1\_ .........
I I
4000 -·r·-3500
- , ~-1· ----- r 3000 2500 2000
Wavenumber (cm-1)
--,----~~ ----- T'-1500 1000
. ----·-·--n· ' I I A
.3
___ ,.,f
I
11\ Spectrum 4 ~~ ~ ~ \ I \II
~ 2 / \ !v \ , 1
1 . ; \\ I Afv!V\ J t 1 \ 1 \A/1\Ji. I "\._ . -.d~
o- ; -~t\ I I ....... ..,.;v ·-~· ----~,.)
t ............................... r .. ·---·--··-··r-- .................. ;- ··-- .c - , --- • .............. r···--·--------··----.,...--................ ..J 4000 3500 3000 2500 2000 1500 1000
Wavenumber (cm-1)
F'igure B26 Spectra of sub-samples of Lactobacillus helveticus, replicate 1 00 --..)
.3
~ .2
i .1
0
3500
~
.3-i
'........:.·, vv
3000
Spectrum 1
-...
2soo 2doo Wavenumber (cm-1)
Spectrum 3
~ j\ ' ~ t ~\ { v \
~--.:1 1..-f _/
1 1500 1000
I '!
j \ ;! IIi
_J
a .2i
I j I I .1 , ' I\ J, ' ) ~·-, ' '" I 0·1 ·~·-""'-/'-..w. .••• .-· ·-...,V·> '· . i . j V \ . r-~·~··· ., ·---·····-~ ,; .. ., ....... ~ \ . .,;..._;.._-~ '"v
~-. -....-c..-r--~---.,-, ___ ...... ...,--·-·····-r-··~·· .. ·-.......,-- ---·.......----J 4000 3500 3000 2500 2000 1500 1000
Wavenumber (cm-1)
I
.3 j
r <( .1
Spectrum 2
l I
I
0 1 .. • "'-.A.-..~._.---·r ·-.. 'Yv- r,~ f\..A -;..,_, __ j ~'--n··-ft-··-- •.,..._r"""'l'l' \.,./-----~·...__..,. ~-~~--........_~~
L. 4000
.3 ..
8 .2
J .1
~ ... } \ ._
0
. r 3500
r .. 3000
' 1 2500 2000
Wavenumber (cm-1)
Spectrum 4
I 1500 ' 1000
A rl ·v i ~ .. "'- ~~ j ' \ r--"'·./'·---·"'--., .v ,.or \,v ·····~--.~J \. .. _ .. ___ ~,_·, . '·\,
r----r--.. --,.... ·· '')- .... -·~--,. .....---·-· 4000 3500 3000 2500 2000 1500 1000
Wavenumber (cm-1)
Figure B27 Spectra of sub-samples of Lactobacillus helveticus, replicate 2
00 00
89
APPENDIX C: Spectral Distances
90
Table Cla Spectral Distances Codes used can be found at end of tables
NAME AlOO A111 A121 A122 Al30 A131
AlOO 0 914.8267 92.65536 85.2563 363.3253 262 .0135
A111 914.8267 0 578.7174 592.5657 207 .9689 315.0659
A121 92.65536 578 .7174 0 0.345221 144.2393 82.91543
A122 85.2563 592.5657 0.345221 0 148.3573 84.54668
Al30 363.3253 207.9689 144.2393 148.3573 0 13.15037
Al31 262.0135 315.0659 82.91543 84.54668 13.15037 0
A132 399.2027 174.6404 183 .9906 189.9291 12.90345 39.24296
Al33 383.8191 188.9464 166.0412 170.953 4.077943 24.32068
A200 580.9337 148.9062 258.0234 272.236 122.7387 163.6316
A201 667.5596 1884.109 969.9916 954.7706 1442.196 1292.242
A202 676.2254 1822.708 927.8761 918.358 1428.488 1285.343
A203 544.4716 1758.739 784.0745 772.2416 1286.665 1133.699
A300 105 .2154 561.1038 45.50698 47.62963 164.1585 115.5848
A400 293 .1633 261.1181 110.7415 117.1517 36.5876 43 .12145
A402 72.52281 604.4677 8.499223 8.618046 162.8767 99.71661
A403 133.8045 452.6158 18.20088 20.19656 83.49217 45.72483 r--ASOO 758.3509 1876.646 1014.556 1002.97 1487.168 1343.798
A600 416.3293 150.2154 168.1583 176.7352 23.45253 53 .63725
A601 194.2278 375.3926 34.05509 37.31802 51.4458 26.73014
A602 192.2691 378.4069 32.76458 36.27619 54.601 29.61791
A700 237.6268 378.1741 45 .22441 51.67212 90.83351 68.15673
A701 119.4562 607.4459 30.83539 27.30011 140.0318 71.39188
A702 151.2076 513 .1469 27 .35588 26.09458 93.06618 39.8961
A703 124.3579 1179.708 213.0104 203.0124 570.5969 432.5922
A801 98.01992 608.4081 11 .07236 8.994102 149.7433 81.58859
A802 69.13953 800.9086 43 .62086 38.19843 268.3275 172.0432
A901 122.7916 1060.247 167.9927 158.1269 478 .6005 353.7141
A902 598.1168 1636.616 750.3255 746.005 1247.055 1111.38
AS aline 1015.089 43 .98504 723 .1808 733.2013 302.1969 421.9306
91
Table Clb Spectral Distances Codes used can be found at end oftab1es
NAME Al32 Al33 A200 A201 A202 A203
AlOO 399.20267 383.81909 580.93366 667.55961 676.22543 544.47155
Alll 174.64039 188.94645 148.90618 1884.1094 1822.7076 1758.7388
A121 183.99061 166.04124 258.0234 969.99161 927.8761 784.07454
A122 189.92906 170.95296 272.23604 954.77061 918.35805 772.2416
Al30 12.903448 4.0779426 122.73868 1442.1956 1428.4882 1286.6652
Al31 39.242956 24.320678 163.63 159 1292.2418 1285.3432 1133.6993
Al32 0 3.1473302 129.66641 1511.9609 1492.5915 1368.4016
A133 3.1473302 0 128.59183 1481.1949 1468.5011 1334.357
A200 129.66641 128.59183 0 1539.6376 1406.9978 1322.3783
A201 1511.9609 1481.1949 1539.6376 0 38.445426 34.101373
A202 1492.5915 1468.5011 1406.9978 38.445426 0 22.3531 A203 1368.4016 1334.357 1322.3783 34.101373 22.3531 0
A300 162.96238 165.55418 288.13078 1031.4798 983.39797 869.56784 A400 23.805279 29.243584 130.90453 1389.1125 1344.9143 1226.5425
A402 189.84332 178.29832 285.13647 949.88116 907.50548 772.78371
A403 102.54409 94.090928 200.4145 1121.2673 1080.4098 941.66192
ASOO 1557.8632 1526.5974 1501 .9592 21.57168 22.635719 39.971653
A600 20.407208 21.533466 59.318072 1521.0154 1460.1824 1341.6883
A601 75.989005 64.35259 143.1085 1202.7826 1154.7993 1014.5507
A602 77.716459 66.920551 142.63019 1200.1732 1149.2869 1011.0544
A700 119.28467 109.16119 102.06521 1199.5373 1108.0045 984.27926 A701 203 .08577 172.64548 310.0973 933.69678 932.01195 767.86395
A702 147.01458 121 .0808 241.6909 1037.4037 1023.055 863 .60703 A703 658.6029 618.88032 735.372 16 389.53096 392.58954 268.48231
A801 207.27403 180.01006 285.78919 928.43735 905.14962 748.13365
A802 341 .63225 307.05946 423.67555 729.15043 719.32088 563.50988
A901 575.95382 529.98115 634.8106 487.33907 493.98869 347.59387
A902 1318.9087 1291.6189 1140.7864 140.11766 46.454868 52.858531 ASaline 266.50596 281.27888 311.77751 1921.9802 1916.5468 1847.3549
92
Table Clc Spectral Distances Codes used can be found at end of tables
NAME A300 A400 A402 A403 ASOO A600
AlOO 105.21537 293.16328 72.522814 133.80449 758.35092 416.32925
Alll 561.10382 261.11805 604.46767 452.61583 1876.6459 150.21542
A121 45 .506975 110.7415 8.4992227 18.200884 1014.5559 168.15825
Al22 47.629634 117.15173 8.6180459 20.196562 1002.9698 176.73523
Al30 164.15848 36.587605 162.8767 83.49217 1487.1682 23.452529
Al31 115.58482 43.121454 99.716605 45.724834 1343.798 53.637248
Al32 162.96238 23.805279 189.84332 102.54409 1557.8632 20.407208
A133 165.55418 29.243584 178.29832 94.090928 1526.5974 21.533466
A200 288.13078 130.90453 285.13647 200.4145 1501.9592 59.318072
A201 1031.4798 1389.1125 949.88116 1121.2673 21.57168 1521.0154
A202 983.39797 1344.9143 907.50548 1080.4098 22.635719 1460.1824
A203 869.56784 1226.5425 772.78371 941.66192 39.971653 1341.6883
A300 0 78.476507 20.948136 26.9075 1087.5859 167.01602
A400 78.476507 0 106.5963 46.928155 1429.028 27.821244
A402 1-20.948136 106.5963 0 15 .651004 1001.9584 180.83529
A403 26.9075 46.928155 15.651004 0 1173 .78 16 94.944751
ASOO 1087.5859 1429.028 1001.9584 1173.7816 0 1545.3392
A600 167.01602 27.821244 180.83529 94.944751 1545.3392 0
A601 58.306698 39.827797 41.374933 9.6883868 1244.2521 59.773823
A602 54.502791 38.620597 39.334615 8.825237 1240.3362 60.6958
A700 84.840446 66.303024 61 .556508 34.166175 1213.7445 74.669376
A701 108.505 161.16065 48.827614 54.862922 992.2878 206.29288
A702 94.867778 113.41338 46.479259 36.044861 1089.1329 146.44062
A703 306.94075 552.52297 216.78529 318.65033 439.19859 659.79697
A801 81.034101 147.36535 24.95308 36.480273 978.40939 193.87553
A802 131.40561 263 .00828 56.130315 100.65178 781.79064 331.2162
A901 286.00421 486.74246 179.87746 262.2589 536.84362 562.44989
A902 828.60498 1159.3043 740.47891 900.63239 91.21611 1248.1377
ASaline 709.45291 379.53566 753 .37193 588.20594 1948.5701 262.95033
93
Table Cl d Spectral Distances Codes used can be found at end of tables
NAME A601 A602 A700 A701 A702 A703
AlOO 194.22777 192.2691 237 .62677 119.45625 151.20757 124.35787
Alll 375.39259 378.40694 378.17405 607.44589 513.14686 1179.7078
A121 34.055086 32.764581 45 .224414 30.83539 27.355879 213.01042
A122 37.318023 36.276187 51.67212 27 .300108 26.094583 203.01241
Al30 51.445801 54.600999' 90.833512 140.03178 93.066178 570.5969
Al31 26.730141 29.617906 68.15673 71.391877 39.896104 432.59222
Al32 75.989005 77 .716459 119.28467 203 .08577 147.01458 658.6029
A133 64.35259 66.920551 109.16119 172.64548 121.0808 618.88032
A200 143.1085 142.63019 102.06521 310.0973 241.6909 735.37216
A201 1202.7826 1200.1732 1199.5373 933 .69678 1037.4037 389.53096
A202 1154.7993 1149.2869 1108.0045 932.01195 1023.055 392.58954
A203 1014.5507 1011.0544 984.27926 767.86395 863 .60703 268.48231
A300 58.306698 54.502791 84.840446 108.505 94.867778 306.94075
A400 39.827797 38.620597 66.303024 161.16065 113.41338 552.52297
A402 41.374933 39.334615 61.556508 48.827614 46.479259 216.78529
A403 9.6883868 8.825237 34.166175 54.862922 36.044861 318.65033
ASOO 1244.2521 1240.3362 1213 .7445 992.2878 1089.1329 439.19859
A600 59.773823 60.6958 74.669376 206.29288 146.44062 659.79697
A601 0 0.1864575 22.155852 60.835127 34.023601 368.42488
A602 0.1864575 0 20.422778 63.25093 36.172963 368.19016
A700 22.155852 20.422778 0 90.403808 59.223384 378.76023
A701 60.835127 63.25093 90.403808 0 6.0686484 173.3264
A702 34.023601 36.172963 59.223384 6.0686484 0 233 .65293
A703 368.42488 368.19016 378.76023 173.3264 233.65293 0
A SOl 45.511349 46.473979 67.571875 10.168577 14.356365 177.39116
A802 124.32322 125.21396 145.77764 28.872732 54.713936 81 .628349
A901 293.67196 295.65247 313.42889 123.25471 176.68381 23.657871
A902 957.22749 950.91045 883.82956 778 .74061 854.07714 319.89553
ASaline 513.72033 518.84518 537.93441 730.3806 640.27457 1312.0651
94
Table Cle Spectral Distances
NAME A801 A802 A901 A902 ASaline AlOO 98.019924 69.139531 122.79164 598.11683 1015.0891
A111 608.40807 800.90857 1060.2469 1636.6158 43.985036
A121 11.072363 43.62086 167.99267 750.32548 723.18083
Al22 8.9941018 38.198427 158.12695 746.00503 733.20126
Al30 149.74333 268.32751' 478 .60053 1247.055 302.19687
Al31 81.588586 172.04318 353.71414 1111.3795 421.93062
Al32 207.27403 341.63225 575 .95382 1318.9087 266.50596
Al33 180.01006 307.05946 529.98115 1291.6189 281.27888
A200 285.78919 423.67555 634.8106 1140.7864 311.77751
A201 928.43735 729.15043 487.33907 140.11766 1921.9802
A202 905 .14962 719.32088 493 .98869 46.454868 1916.5468
A203 748.13365 563 .50988 347.59387 52 .858531 1847.3549
A300 81.034101 131.40561 286.00421 828.60498 709.45291
A400 147.36535 263 .00828 486.74246 1159.3043 379.53566
A402 24.95308 56.130315 179.87746 740.47891 753 .37193
A403 36.480273 100.65178 262.2589 900.63239 588.20594
ASOO 978.40939 781.79064 536.84362 91.21611 1948.5701
A600 193.87553 331.2162 562.44989 1248.1377 262.95033
A601 45 .511349 124.32322 293 .67196 957.22749 513 .72033
A602 46.473979 125.21396 295.65247 950.91045 518.84518
A700 67.571875 145.77764 313.42889 883 .82956 537.93441
A701 10.168577 28.872732 123.25471 778.74061 730.3806
A702 14.356365 54.713936 176.68381 854.07714 640.27457
A703 177.39116 81 .628349 23 .657871 319.89553 1312.0651
A801 0 21.17552 120.8495 735 .08162 745 .31634
A802 21.17552 0 44.984153 576.70404 936.87694
A901 120.8495 44.984153 0 390.32486 1187.6601
A902 735 .08162 576.70404 390.32486 0 1795.3268
ASaline 745.31634 936.87694 1187.6601 1795.3268 0
95
Table C2 Codes Used in Spectral Distances Tables
Code N arne of Bacteria AlOO Bacillus subtilis Alll Escherichia coli A121 Lactobacillus casei 202 (replicate 1) Al22 Lac;tobacillus casei 202 (replicate 2) ABO Pseudomonas fluorescens (replicate 1) Al31 Pseudomonas fluorescens (replicate 2) Al32 Pseudomonas fluorescens (replicate 3) Al33 Pseudomonas fluorescens (replicate 4) A200 Lactococcus lac tis ssp. cremoris (replicate 1) A201 Lactococcus lactis ssp. cremoris (replicate 2) A202 Lactococcus lactis ssp. cremoris (replicate 3) A203 Lactococcus lactis ssp. cremoris (replicate 4) A300 Leuconostoc A400 Pediococcus pentosaceus (replicate 1) A402 Pediococcus pentosaceus (replicate 2) A403 Pediococcus pentosaceus (replicate 3) A500 Escherichia coli A600 Pediococcus acidiladici (replicate 1) A601 Pediococcus acidiladici (replicate 2) A602 Pediococcus acidiladici (replicate 3) A700 Lactococcus lac tis ssp lac tis (replicate 1) A701 Lactococcus lactis ssp lactis (replicate 2) A702 Lactococcus lactis ssplactis (replicate 3) A703 Lactococcus lactis ssp lactis (replicate 4) A SOl Lactobacillus casei 201 (replicate 1) A802 Lactobacillus casei 201 (replicate 2) A901 Lactobacillus helveticus (replicate 1) A902 Lactobacillus helveticus (replicate 2) ASaline Background spectrum