Identification of paper and influence of its homogeneity
in forensic investigations by ICP-AES/MS and other
non-invasive spectroscopic techniques
Doreen-Marie Shamon
Master’s program in Forensic Science
Uppsala University
2012-06-05
Abstract
The purpose with this thesis was to find out if ordinary office paper was homogenous
throughout the sheet in context of organic and elemental content. Two different types of
papers were also analyzed, paper towel and corrugated paperboard. The techniques used
were NIR, FT-IR and Raman spectroscopy for the evaluation of organic contents, and
ICP-AES/MS for the measurement of elemental composition and concentration.
Spectrofluorometry was also utilized to establish if the content of optical brightener was
homogenous in all paper types. The spectroscopic techniques didn’t require any sample
preparation except for cutting the papers in pieces, according to their “geographical”
place in the sheets. The ICP-AES and ICP-MS analyses required sample preparation in
form of cutting the pieces and digesting each of them with acid and hydrogen peroxide in
digestion bombs. After the digestion the samples were diluted with purified water. The
results showed that NIR and spectrofluorometry couldn’t differentiate samples within one
sheet of all paper types, although NIR made a distinction between the office paper
samples. FT-IR on the other hand could distinguish between samples in one group from
samples belonging to another group further away within same sheet. The elemental
concentrations of sample pieces were also significantly different within same sheet of
office paper, paper towel and corrugated paperboard. This elemental distinction could be
made in both ICP-AES and ICP-MS. The results from Raman spectroscopy were
unusable due to gained broad bands as spectra instead of peaks, the reason for that is high
fluorescence. Different laser intensities were used with no change in result.
Sammanfattning
Syftet med arbetet var att undersöka homogeniteten hos papper med avseende på
sammansättningen av grundämnen, organiska komponenter samt optiskt vitmedel. Tre
olika sorters papper användes A4 papper, hushållspapper och wellpapp. Tekniker som
användes för detta syfte var ICP-AES (inductively coupled plasma-atomic emission
spectroscopy) och ICP-MS (inductively coupled plasma-mass spectroscopy) för
koncentrationsbestämning av element. NIR (near-infrared spectroscopy) , FT-IR (fourier
transform-infrared spectroscopy) och Raman spektroskopi användes för analys av de
organiska komponenterna i pappret samt spektrofluorometri för mätning av optiskt
vitmedel och andra fluorescerande molekyler i pappret. Provupparbetning krävdes för
analys med ICP-AES och ICP-MS. De olika papperstyperna upplöstes med olika
sammansättning av syra och väteperoxid i Uhrbergsbomber, efter upphettning kyldes
proverna till rumstemperatur och späddes med milli-Q vatten. Samma prover som
användes för ICP-AES analyserna späddes med thallium internstandard innan analys med
ICP-MS. De andra spektroskopiska teknikerna krävde ingen provupparbetning, annat än
urklippning av pappersbitar som skulle passa provbehållaren. Resultaten visade att ICP-
AES/MS kunde diskriminera olika element i olika koncentrationer genom ett helt ark.
Detta gällde för alla papperstyper. FT-IR var också diskriminernande för vissa prover
inom alla papperssorter, medan resultaten från NIR och spektrofluorometri analyserna
inte kunde urskilja olika prover inom ett och samma ark. Detta tyder på att dessa
instrument inte kan uppvisa någon inhomogenitet hos pappret med avseende på organisk
sammansättning respektive innehållet av optiskt vitmedel.
Table of contents
Section Page
1. Introduction 2
2. Theory 4
3. Materials and methods 8
4. Results 11
5. Discussion 47
6. Conclusion 50
7. Future aspects 50
1
Abbreviations
ANOVA Analysis of variance
FT-IR Fourier-transform infrared spectroscopy
ICP-AES Inductively coupled plasma- Atomic emission spectroscopy
ICP-MS Inductively coupled plasma-Mass spectrometer
NIR Near-infrared spectroscopy
PCA Principal component analysis
2
1. Introduction
Discrimination of different paper documents have become important due to many crimes
committed with forged documents like wills, checks etc hence its importance to correlate,
for example one piece of paper from crime scene to the rest of the sheet found elsewhere.
The most common techniques used for this task is ICP-MS because of its ability to
identify and measure the concentration of different elements at the same time.
Different analyzing techniques have also been studied in this thesis to investigate the
homogeneity throughout the same sheet of paper of different kinds of fiber containing
materials like office papers, paper towels and corrugated paperboard. Techniques used
are ICP-MS (inductively coupled plasma-mass spectrometry), ICP-AES (inductively
coupled plasma- atomic emission spectroscopy), FTIR (Fourier transform infrared
spectroscopy), NIR (near-infrared spectroscopy), spectrofluorometry and Raman
spectroscopy. These techniques measures different kinds of analytes throughout the paper
sheets; FT-IR, Raman spectroscopy and NIR mainly reflects the organic contents in paper
and ICP-AES and ICP-MS measures elemental composition and concentration. The IR
spectroscopic techniques are non-invasive; therefore the samples can be stored and
reanalyzed again at different occasions.
The main purpose of this forensic investigation, was to study the homogeneity within a
paper and how this result could affect real crime cases involving paper found at crime
scene and piece of paper found with suspect. This is important because if it shows that
two pieces of paper originating from the same sheet don’t have same the same elemental
composition or concentration (statistically proven different) through the whole paper then
one can not be able to link the evidence from crime scene with the suspect. The statistical
tool used for this comparison of different groups of samples within one paper sheets is
ANOVA (analysis of variance).
Previous researches done in the area have already proven that ICP-MS can distinguish
between different brands of paper and even between different reams, from the same brand
by elemental concentration and composition [1]. It makes sense that different brands of
paper have different elemental concentration due to different raw material sources used in
the manufacturing process of paper, but in this paper the main purpose was to investigate
the homogeneity within the same sheet of paper and how small pieces of paper could be
used and still be able to connect the piece of paper with the rest of the sheet.
The authors in the article selected two different vendors of paper and took five reams
from each brand. From every ream three sheets were chosen for analysis, taken from the
top, bottom and in the middle of the reams. For every sheet five samples were taken one
from each corner and one from the middle of the paper each piece with a weight of
0.029 ٕ ± 0.001g. The area of each piece was 23 *18mm. The samples were put in a
quartz tube were 1.5 mL of nitric acid and 0.75 mL of hydrogen peroxide were pipetted.
The quartz tube was set into a Teflon vessel, which was filled with 11 mL water and 1
mL of hydrogen peroxide. The paper samples were microwave digested from ambient up
3
to 210°C for fifteen minutes and then hold for yet ten minutes before cooling. The
samples were analyzed with ICP-MS. The results obtained showed that the papers could
be discriminated into different vendors, different batches or reams from the same vendor
but not between the three sheets within same ream, or within a single sheet. Different
elements were discriminating between the two vendors and the reams; overall the
elements that were more discriminating were Al, Ba, Sr, Mg, Mn and Ce.
In another article they chose seventeen different brands of paper (five sheets from each
brand) for analysis; the papers were cut from the outer edge of each side of the paper [2].
The sample sizes were cut 3*4 cm with an approximate weight of 0.1 to 0.11 g. The
specimens were each added 3 mL of sub-boiled nitric acid and 1.5 mL hydrogen
peroxide, after digestion the samples were diluted to 40mL water with purified water.
The results from this study showed that different brands of paper can be discriminated by
elemental composition and concentration. In this case nine elements were discriminative:
Na, Mg, Al, Mn, Sr, Y, Ba, La, and Ce. Different batches from the same manufacturer
could also be distinguished by three elements Al, Zr and Mn.
According to an article about classification of papers, they could successfully distinguish
eight different brands of paper by FT-IR analysis in ATR mode; eight samples were cut
from each brand [3]. The spectra ranges chosen for the analysis were divided in two sets
4000-2000 and 2000-650 cm-1
but the whole spectra were taken into account for the
evaluation of the results with PCA. The most discriminating region with PCA was at
4000-2000 cm-1
because this is where kaolin absorbs strongly, approximately around
3700 cm-1
. Organic compounds like cellulose, hemicellulose, lignin and inorganic fillers
like kaolin absorb in the infrared region, thus FT-IR is also an important technique for
discrimination of different kinds of paper [4]. Raman spectroscopy discriminate also
papers by their content of inorganic fillers.
Another study showed that nineteen different brands of papers could be differentiated by
FT-IR [5]. Five samples from every sheet were cut for the analysis, a total of five sheets
from every brand were sampled, and each side of the paper was measured. The spectra
range chosen for the analysis was between 4000-650 cm-1
. The papers could be
distinguished by cellulose and calcium carbonate (CaCO3) peaks in the spectrum.
Calcium carbonate is added to the paper as inorganic filler to control the characteristics of
paper. Cellulose and calcium carbonate were detected in the spectra range 750-1550 cm-1
.
NIR is a technique commonly used in pulp and paper industries to determine the organic
content and quality of the wood and pulp. The organic contents to be controlled in the
industries are cellulose, lignin, pentose and the total pulp yield [6-7].
4
2.1 Theory
2.2 Paper manufacturing
Paper is made by firewood and consists of cellulose and hemicellulose. These fibers are
held together by lignin which is the “natures glue” for the fibers. All of these organic
compounds consist of carbon, hydrogen and oxygen atoms. It is not unusual that the
paper also contains added kaolin as filler to improve the characteristics of paper, for
instance brightness and texture after the paper pulp process [8]. Paper also contains
inorganic elemental compounds because of the trees used for production of papers
absorbs elements from the ground water [1].
There are two ways of producing papers either by mechanic or by chemical release of the
fibers from the wood into paper pulp [9]. The method chosen depends on the type of
paper to be manufactured. Mechanical release of the fibers usually consists of grinding
the wood into pulp. Papers produced only by this method are darker, thinner and less
strong, like for example magazine papers. Due to the lignin content the paper becomes
yellowish after a while. Office papers, which are brighter and have a better printing
surface are manufactured using the chemical pulping process. The wood chips are boiled
in a chemical solution (either acidic or basic) under high pressure. Depending on which
solution is chosen for paper pulp manufacturing; either basic or acidic, the methods are
divided into sulphate and sulphite mass. When it comes to the sulphate method, sodium
hydroxide and sodium sulphide solutions are added. For the sulphite method either
magnesium or sodium bisulphite solution with pH 4 is added. Production of, for instance
corrugated paperboard uses a semichemical method using neutral sodium sulphite
solution. The chemical boiling time is shorter and the mass is chopped afterwards.
The paper pulps are thereafter dried and bleached. If the mechanical method is used, the
pulp is bleached with hydrogen peroxide on the other hand if the chemical method is used
the lignin is removed as much as possible. Before entering the paper machine the fibers
are mixed with water to create furnish. The furnish is applied to a plastic sheeting to
remove most of the water before the fibers are pressed to form paper and then further
dried see figure1.
Figure 1: Picture of a paper machine, with a wet section, press section, drying section and a paper roll up
section [9].
5
2.3 ICP-MS/ICP-AES
Inductively coupled plasma (ICP) is a technique applied in analytical chemistry to
analyze metals in liquid samples [10]. The sample is introduced to the torch by a
nebulizer that breaks the liquid into a fine mist of droplets, aerosol. Aerosol created is
dried out due to the heat inside the torch, see figure 2. Before the sample reaches the
torch, aerosol is passed through a spray chamber which first separates smaller droplets
from the larger ones. Only the smaller droplets continue to the torch.
A tesla coil inside the torch gives rise to argon ions Ar+ and free electrons. The heat from
the argon gas atomizes the molecules and the atoms are ionized in plasma before
introduction to interface. The ions pass through a mass filter before reaching the detector
an electron multiplier. The mass filter in ICP-MS is a mass spectrometer; it separates the
ions by their mass to charge ratio m/z [11]. The separation of ions occurs between four
metal rods on which direct and alternating current are applied on each pair of rods. By
choosing a certain AC/DC current on the rods only a specific m/z will pass through the
mass filter and reach the electron multiplier. All the other ions will strike against the rods
and will not be detected.
Figure 2: An overview of the ICP-MS instrument [11].
When using atomic emission spectroscopy (AES) as a detection method, the electrons in
the atoms are excited to a higher energy level by the plasma, and when the electrons
returns to ground state, energy is released in forms of photons. The detector, a
photomultiplier detects the emission of light released. Different elements releases energy
at different wavelengths and so the elements are identified and detected. The grade of
intensity indicates the concentration of the element in the sample.
When comparing the two techniques; ICP-MS is more sensitive than ICP-AES. It can
detect concentration levels down to parts per trillion (ppt) whereas ICP-AES can detect
6
parts per billion (ppb). ICP-MS only detects ions with selected m/z, and leaves out all the
rest, by doing so the noise signal is diminished.
2.4 Raman spectroscopy, FT-IR and NIR
Raman spectroscopy and Fourier transform infrared spectroscopy (FT-IR) works in a
similar way, the sample is irradiated with mid-infrared laser, for Raman even near-
infrared and visible light is used [12]. The sample molecule absorbs the transferred
energy, and excites to a higher energy level. The molecule starts to vibrate and rotate in
the same oscillating frequency as the photon that stroke it. When the molecule has a
dipole moment it is IR active but Raman inactive. The IR spectra plot shows the ratio of
absorbed or emitted energy from the molecule versus the wavenumber (cm-1
). This
relationship is proportional to the vibrational energy difference between the ground state
and the excited energy level.
Samples analyzed with Raman spectroscopy must be polarizable; it means that the
electron cloud surrounding the molecule will be changed when an outer electrical field is
applied with two oppositely charged plates. It induces a dipole moment, due to the
electrical field, see figure 3. Raman spectral plots give the same results as FT-IR spectra
but reversed. Unlike other spectroscopic techniques Raman measures inelastic light
scattering (Stokes scattering, how much of the energy is lost) for quantification.
Sometimes the energy is gained during irradiation and detects anti-stokes light scattering.
FT-IR and Raman spectroscopy complements each other due to FT-IR measures
asymmetrical and polar analytes while Raman spectroscopy measures symmetrical
bondings of non-polar analytes.
Figure 3: Induced dipole moment with Raman spectroscopy. [12]
Near-infrared spectroscopy measures overtones and combination of vibrational
transitions of the molecule irradiated with IR laser. NIR spectroscopy gives information
about the atoms in the molecule like FT-IR and Raman spectroscopy. This is known as”
fingerprinting”. The difference between NIR, FT-IR and Raman is that NIR measures in
7
the near-infrared region whereas FT-IR and Raman spectroscopy are both measured in
the mid-IR region.
2.5 Spectrofluorometry
When a sample is irradiated with either visible or ultraviolet (UV) light, the molecules in
the samples absorbs the light and excites from ground state to a higher energy level [10].
The molecule is then in an unstable state and strives towards stability. This is when
relaxation occurs which means that the molecule falls back to the ground state, when it
does it emits light i.e. fluorescence. Fluorescence is measured between 500-700 nm. This
spectroscopic technique works for analysis of for example optical brighteners which
emits fluorescence light.
2.6 PCA and ANOVA
In this experimental work the purpose was to establish if paper samples taken near each
other were statistically different from other samples at a longer distance within the same
paper. Different techniques used as mentioned above give multi-variate responses from
the sample analyses. The variables can be evaluated with PCA (principal component
analysis) calculated with the UNSCRAMBLER software. PCA score plots are used when
the numbers of variables are too extended to be evaluated in one chart [13]. New
variables (principal components) are created from the old variables and their coefficients
(loadings). The coefficients maintains as much of the information from the variables as
possible. Each sample result consists of score values for every component. The variation
between samples is larger in the first principal component and declines with increasing
number on the principal component. The scores are then plotted pairwise against each
other in a scatter plot. Samples similar in composition tend to be near each other while
samples different from each other are separated in the score plot. With a loading plot it is
possible to find out which variables that are contributing to the differences.
However PCA gives only an indication visually if the samples are different from each
other or not. To be certain of the conclusions made from the analysis a statistical
calculation called ANOVA (Analysis of variance) is performed, either on the score
values or the original variables. This mathematic tool compares the variances calculated
for between and within groups of samples [14]. The comparison is implemented by
dividing the between and within values with each other and hence forming the F value
(Fishers value). The null hypothesis is that there is no true difference between groups, the
F value should then not be larger than 1. If the F value is greater than the critical value or
the P value (probability value) is below the significance level, in this thesis at 5% or 0,05
the null hypothesis is rejected and there is significant difference between the groups.
8
3. Materials and methods
3.1 Materials
Digestion bombs were used to decompose the papers into the solution before ICP
analysis. The samples were diluted in pyrex tubes. Ceramic knife (Satake) and ceramic
scissors (Kyocera) were used to cut the papers before digestion. Three different types of
papers were used Multicopy office papers 80g/m2 (Stora enso), paper towels and
corrugated paperboard.
3.2 Chemicals
65% sub-boiled nitric acid (Merck), 30% hydrogen peroxide (JT Baker), 95-97%
sulphuric acid (Merck) and milli-Q purified water were the solutions used for sample
digestion and dilution for the wet chemical techniques.
3.3 Softwares, instruments and settings
The UNSCRAMBLER and MINITAB were the softwares used for statistical
calculations.
3.3.1 NIR
NIR inframatic 8620 from Percon was used for the paper analyses. Twenty filters were
used and each filter measured one wavelength. The wavelengths were 2345, 2336, 2310,
2270, 2230, 2208, 2190, 2180, 2139, 2100, 2050, 1982, 1940, 1818, 1778, 1759, 1734,
1722, 1680 and 1445 nm.
3.3.2 FT-IR
FT-IR Spectrum 2000 PerkinElmer instrument was used for substances reflecting infrared
spectra. The spectra were measured in the range 5000-500 cm-1
and the resolution was
2.0. The interval between each measurement was 0.5 cm-1
.
3.3.3 ICP-AES
The instrument used for paper analysis was ICP-AES Spectro Ciros CCD, PerkinElmer.
The gases used in ICP-AES were nebulizer gas with a flow of 0.9 l/min, auxiliary gas
with 0.9 l/min flow and coolant gas with a flow of 14 l/min. The ICP RF power was
1400W.
3.3.4 ICP-MS
ICP-MS NexION 300D from PerkinElmer was used for the elemental determination in
papers. The gases and their respective flows used for ICP-MS analysis were nebulizer gas
(1 l/min), auxiliary gas (1 l/min) and coolant gas (15 l/min). The ICP RF power was set
to1600W.
9
3.3.5 Spectrofluorometry
Spectrofluorometer FP-750 from JASCO was used for measurement of fluorescence
emission. The sample molecules in the papers were excited at 350 nm. The fluorescence
emission was measured between 390-700 nm.
3.3.6 Raman spectroscopy
Paper samples were analyzed with Reinshaw ramascope; Leica DMLM . Sample
molecules were excited with 514 nm laser. The time of sample exposure was 10s and
each sample was measured once. Different laser powers were used 100, 50, 25, 10 and
1%. The spectra were collected in the range 100-3200 cm-1
.
3.4 Sampling and sample preparation ICP-AES
For each paper type blanks were made containing only respective acid and hydrogen
peroxide diluted with milli-Q water. Same parameters were used for both samples and
blanks. Standard solution was made containing twenty two elements at 1ppm, with an
exception for calcium at 50 ppm due to high concentrations in the samples. The elements
measured were Al, Mn, Mg, Sr, Li, Na, K, Rb, Ca, Ba, Sc, Y, Ti, V, Cr, Fe, Co, Ni, B, Si,
P and Ce.
3.4.1 Office paper
One sheet of white office paper was cut in squares 2*2cm with ceramic knife. A total of
20 pieces were cut from the paper, four taken from each corner and four from the center
of the paper. These papers were weighted before sample preparation. Each sample was
put in separate pyrex tube were 2mL nitric acid and 1mL of hydrogen peroxide was
added before closing the digestion bomb. The heating program was set to 125°C
(29.3min) with accelerating temperature 10°C/min to a final temperature of 165°C for 90.
1 min. After cooling the samples were diluted to 10 mL with milli Q purified water.
3.4.2 Paper towels The paper towels were cut with ceramic scissors to 4*4 cm to obtain approximately the
same weight as for the office papers. A total of twenty samples were analyzed, four from
each corner of the paper and four from the center of the paper. In each pyrex tube 3mL of
nitric acid and 2mL of hydrogen peroxide were added before heating.
The temperature program was changed to 125°C (45min) as a start temperature and a
final temperature of 168°C for 130 min. After cooling the samples they were diluted to a
final volume of 10 mL with milli Q purified water.
3.4.3 Corrugated paperboard
The sample size of corrugated paper board was 2*2 cm. Altogether 16 samples were cut
with ceramic knife, four from each outer edge of the corrugated paper. To each sample
3mL HNO3 and 2mL H2SO4 was added in a pyrex glass. The Pyrex glasses were heated
in a heating block with a total effect of 638W for two hours. After the digestion the
samples were diluted to 25mL with milli-Q water.
10
3.5 Sampling and sample preparation ICP-MS Paper and paper towel samples made in section 3.4 were reanalyzed with ICP-MS.
Before the analysis the samples were diluted ten times (1mL to 10mL) with an thallium
internal standard (10 ppb, 0.1% HNO3). When it comes to the corrugated paper from
section 3.4.3 the samples were diluted up to 25mL, because of the higher elemental
concentrations in the latter paper.
Standard solutions used were at 0, 5, 10, 20 and 30 ppb of the chosen elements; the
standard solutions were also diluted with 10 ppb thallium solution. Thallium was chosen
because natural materials rarely contain any thallium and will not already be present in
the paper samples.
3.6 Sampling and sample preparation FT-IR analysis
3.6.1 Office paper, paper towels and corrugated paperboard
Samples of twenty pieces were cut altogether, four from each corner and four from the
middle of the paper. Each paper was cut 1*1 cm to fit the sample holder of FT-IR. The
spectra range was 5000-500cm-1
. These are a new set of samples coming from another
office paper sheet than the one used for ICP-AES and ICP-MS analysis. The sheet used
for FT-IR analysis comes from the same ream as for the samples analyzed with the ICP
instruments.
3.7 Sampling and sample preparation spectrofluorometry Twenty new pieces of all paper brands were cut with a ceramic knife with the size 1.2*2
cm and analyzed with the spectrofluorometer. Excitation wavelength was set at 350 nm.
Emission of light was measured in the range 390-700 nm.
3.8 Sampling and sample preparation NIR All the paper types were cut altogether so office paper gave 24 samples, paper towels 20
samples and corrugated paperboard 24 samples. The size of each sample was 4.5*4.5cm
to fit the sample holder, because of the larger size on the sample holder new set of twenty
pieces had to be cut from scratch from a new office paper. The papers were analyzed for
twenty filters and twenty wavelengths.
3.9 Sampling and sample preparation Raman spectroscopy The same sample pieces cut in section 3.6 for the FT-IR analysis were also used for
Raman spectroscopy analysis. Before analysis the instrument was calibrated with a silica
plate. The silica peak was corrected to 521nm if it was offset.
11
4. Results
4.1 Office paper
4.1.1 NIR analysis
One-way ANOVA was calculated from the results obtained from office paper analysis
with groups according to figure 4. The results showed a significant group difference only
for PC1 scores (P=0.002). Another possible reason for significant differences besides the
samples alone is systematic errors. The NIR instrument tends to show different results of
the same sample in the beginning and end of an analysis. This was avoided by
randomized order of analysis.
The papers were cut and the samples divided into columns and rows to recognize their
position on the paper, see figure 4. The division makes it possible to identify differences
or similarities between samples throughout the paper. The similarities or differences
found can depend on the distance between the samples.
A1
B1
C1
D1
E1
F1
A2
B2
C2
D2
E2
F2
A3
B3
C3
D3
E3
F3
A4
B4
C4
D4
E4
F4
Figure 4: Division of the office paper in groups of four samples.
The score plot for the principal components one and two showed a division between
samples from row one and two and row three and four, see figure 5. Figure 6 is showing
an example of a NIR spectrum for sample A1.
12
Figure 5: Score plot of office paper samples, analyzed with NIR spectroscopy.
A1 spectrum
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
1200 1400 1600 1800 2000 2200 2400 2600
Wavelenght (nm)
Re
fle
cte
d i
nte
ns
ity
Figure 6: NIR spectrum of office paper sample A1.
4.1.2 FT-IR analysis
The paper samples were analyzed on both the front and backside. Figure 7 shows that
there are differences for PC2 scores between these two for the same sample. Figure 8
shows the positions where the samples were taken from the paper.
For further data evaluation the front side and backside samples were separated in the
score plots. According to the PCA score plots for the front side samples, one can visually
see that the samples coming from columns nearby in the paper sheet are similar to each
13
other. Samples from column A and B are more situated on the right side of the chart,
while samples from column N and O are situated on the left side on the score plot. G and
H are located somewhere in between N and B. This indicates difference along the long
side of the paper, see figure 9. The corresponding score plot for the backside samples
shows a similar distribution of the samples as for the front side samples, see figure 10.
The A and B samples are in one group and N, O, G and H samples are grouped together
in the PCA plot.
ANOVA calculations were made for principal components 1 and 2 for both front and
backside of the paper. The spectrum range for these calculations was at 3710-500 cm-1
.
ANOVA showed significant difference for the first component of both front and backside
of the office paper. All samples were divided into groups before the statistical
calculations, see table 1 below.
The difference were found between group A and B and the rest of the groups i.e. group A
and B were separated from group C, D and E.
Figure 7: Score plot showing the results from FT-IR analysis of front side and backside of the same office
paper. Spectrum range: 4000-500cm-1
.
14
A1 B1 N1 O1
A2 B2 N2 O2
G1 H1
G2 H2
A10 B10 N10 O10
A11 B11 N11 O11 Figure 8: Sites on the paper from which the samples were taken.
Figure 9: Score plot of front side office paper samples, analyzed with FT-IR spectroscopy. The spectra
range was set at 3710-500 cm-1
.
15
Figure 10: Score plot of backside office paper samples, analyzed with FT-IR spectroscopy. The spectra
range was set at 3710-500 cm-1
.
Sample Group
A1 A
A2 A
B1 A
B2 A
A10 B
A11 B
B10 B
B11 B
G1 C
G2 C
H1 C
H2 C
N1 D
N2 D
O1 D
O2 D
N10 E
N11 E
O10 E
O11 E Table 1: Office paper samples divided into groups before ANOVA calculation
of the FT-IR results.
16
In figure 11 below, a spectrum of sample A1 is shown. The spectra range covers from
5000-500 cm-1
.
A1 spectrum frontside
0
20
40
60
80
100
120
0 1000 2000 3000 4000 5000 6000
Wavnumber cm-1
Re
fle
cte
d i
nte
ns
ity
Figure 11: FT-IR spectrum for sample A1. Front side of the office paper illustrated.
4.1.3 ICP-AES
The division of the samples within the paper is shown in figure 12. ICP-AES results of
the paper samples are shown in the PCA score below, in figure 13. The responses from
ICP-AES for each sample were recalculated to µg/g elemental content in one gram of
paper, see appendix A. There seem to be a random distribution of the samples rather than
an arranged one. Samples from different columns and rows are grouped together, except
for sample A11, B11 and O11, these samples seem to belong to the same group, they
were all taken from the last row in the office paper. In figure 14 the different elements
contributing to the sample differences are illustrated.
One-way ANOVA was calculated on the responses, the results showed that there were
significant differences between the different groups of paper samples. The discriminative
elements were Na, K, Sr, Ti and V. All the elements except Sr showed a discrimination of
sample A10, B10, A11 and B11 from all the rest. While Sr showed that sample A1, A2,
B1, B2, N1, N2, O1 and O2 were more similar to each other from the rest of the samples.
17
A1 B1 N1 O1
A2 B2 N2 O2
G5 H5
G6 H6
A10 B10 N10 O10
A11 B11 N11 O11
Figure 12: Paper positions chosen for analysis with ICP-AES. Four samples taken from each corner and
four samples selected approximately from the middle of the sheet, i.e. a total of five groups
Figure 13: PCA score plot of office paper analyzed with ICP-AES.
18
Figure 14: PCA loading plot showing the elemental contribution of the scores.
Figures 15-16 shows the concentrations of the discriminative elements in µg per one
gram of the weighted paper before the sample digestion.
Elemental concentrations
-100
0
100
200
300
400
500
A1
B1
A2
B2
A1
0
B1
0
A1
1
B1
1
G5
H5
G6
H6
N1
O1
N2
O2
N10
O10
N11
O11
Sample
Co
nc
en
tra
tio
n (
µg
/g)
Na 589.592 Na 588.995 K 766.491 Sr 421.552 V 311.071
Figure 15: Bar chart showing concentration (µg/g) of the discriminating elements in office paper,
analyzed with ICP-AES.
19
Elemental concentration
0
1
2
3
4
5
6
7
8
A1 B1 A2 B2 A10 B10 A11 B11 G5 H5 G6 H6 N1 O1 N2 O2 N10 O10 N11 O11
Sample
Co
ncen
trati
on
(µ
g/g
) Ti 336.121
Figure 16: Bar chart showing the elemental concentration (µg/g) of Ti in office paper sheet, analyzed with
ICP-AES.
4.1.4 ICP-MS
The elements, which gave too low signals to give a reliable result in ICP-AES or were
not included from the beginning, were also analyzed with ICP-MS. These elements were
Ce, Sc, La, Mn, V, W, Zr, Rb, Ti, Mo, Zn, Pb and Cu. After the ANOVA calculation only
V, Rb and Ti were discriminative and gave a significant difference depending on the
spreading of the paper. The significant difference lied in the samples A10, A11, B10 and
B11. ANOVA gave no significant difference between principal component 1 and 2.
PCA score plot of all samples is shown in figure 17, the contribution of the elements are
shown in figure 18. The plot shows no certain pattern for the samples; they all seem to be
randomly distributed. The elemental concentrations were recalculated to ng/g per sample
see appendix A. The concentrations of the discriminative elements are shown in figure
19-21. The concentrations of Ti and Rb are higher in sample A10, A11 and B11.
20
Figure 17: PCA score plot of office paper samples analyzed with ICP-MS. No order between the samples
are shown.
Figure 18: PCA loading plot of the elements contributing to sample scores.
21
Elemental concentration
0
1000
2000
3000
4000
5000
6000
7000
8000
A1
A2
A10
A11
B1
B2
B10
B11
G5
G6
H5
H6
N1
N2
N10
N11 O
1O2
O10
O11
Sample
Co
ncen
trati
on
(n
g/g
)Ti
Figure 19: Concentration of discriminative element Ti (ng/g), analyzed with ICP-MS.
Elemental concentration
0
100
200
300
400
500
600
700
A1 A2A10 A11
B1 B2
B10 B11
G5
G6
H5
H6
N1
N2
N10
N11 O
1O2
O10
O11
Sample
Co
nc
en
tra
tio
n (
ng
/g)
V
Figure 20: Concentration of the discriminative element V (ng/g), analyzed with ICP-MS.
22
Elemental concentration
0
10
20
30
40
50
60
A1
A2
A10
A11
B1
B2
B10
B11
G5
G6
H5
H6
N1
N2
N10
N11 O
1O2
O10
Sample
Co
ncen
trati
on
(n
g/g
)Rb
Figure 21: Concentration of the discriminative element Rb (ng/g), analyzed with ICP-MS. Sample O11
was excluded from the figure, due to negative result.
4.1.5 Spectrofluorometer
Each paper sample was analyzed twice, including front and backside with a
spectrofluorometer to observe if the content of optical brighteners were different
throughout the sheet. The samples were taken from specific positions in the paper. The
samples being closer to each other are considered to be in one group i.e. there are five
groups with four samples in each. These groups are located in the four corners and in the
middle of the paper sheet, see figure 22.
Figure 23 shows no pattern between samples taken close or further away from each other,
the same resolution applies even with the frontside and backside are separated in the
chart, see figure 24-25. Frontside and backside of the same samples gave different results
as the first PCA score plot exhibits. ANOVA calculations on both PC1 and PC2 gave no
significant difference between groups.
A1 B1 N1 O1
A2 B2 N2 O2
G1 H1
G2 H2
A10 B10 N10 O10
A11 B11 N11 O11 Figure 22: Sample position on office paper before analysis with spectrofluorometer.
23
Figure 23: PCA score plot of office paper analyzed with spectrofluorometer. Frontside and backside of
paper measured.
Figure 24: Score plot of office paper analyzed with spectrofluorometer. Frontside measured.
24
Figure 25: Score plot of office paper analyzed with spectrofluorometer. Backside measured.
The fluorescence spectrum of sample A1 is shown in figure 26. The peak is high which
implies a high concentration of optical brighteners in the paper.
A1 spectrum
0
50
100
150
200
250
300
350
400
450
200 300 400 500 600 700 800
Wavelenght (nm)
Flu
ore
scen
ce e
mis
sio
n
(in
ten
sit
y)
Figure 26: Sample A1 spectrum of office paper analyzed with spectrofluorometer.
25
4.2 Paper towels
4.2.1 NIR analysis
The paper towels were cut altogether with 20 samples divided into five columns and four
rows pointing out their position on the paper, see figure 27.
A1 B1 C1 D1 E1
A2 B2 C2 D2 E2
A3 B3 C3 D3 E3
A4 B4 C4 D4 E4
Figure 27: Positions were samples are cut from the paper towel.
The score plot in figure 28 shows a vague separation between samples of row 1 and 2
located at the left side of the chart and 3 and 4 located on the right side. The exceptions
are the samples D2, A3 and C3, which are situated on opposite sides within the chart. The
one-way ANOVA calculated showed no statistical difference between the groups of
samples. Figure 29 illustrates an example of a NIR spectrum the sample chosen is A1.
Figure 28: Score plot of paper towel samples analyzed with NIR.
26
A1 spectrum
0
0.1
0.2
0.3
0.4
0.5
0.6
1200 1400 1600 1800 2000 2200 2400 2600
Wavelenght (nm)
Refl
ecte
d in
ten
sit
y
Figure 29: NIR spectrum of paper towel sample A1.
4.2.2 FT-IR analysis
Samples in the paper towel had a named position in the paper before the analysis, see
figure 30. The results of the analysis of paper towel show differences between the front
and backside. The samples illustrated in the score plot are further away from each other
while they should be at the same spot, see figure 31. The range of the spectrum was cut
down to 4000-500cm-1
from the original 5000-500cm-1
, to remove excess information
that were the same in all samples. As the figure 31 shows the samples taken from one
side of the paper; from column A and B are situated in the right side of the plot while the
rest are positioned at the left side of the plot.
Two more PCA plots were done for front and backside of the paper each to interpret the
samples better, figure 32-33. ANOVA calculations showed significant difference for
principal component 1 for both front and backside of the paper. The samples closer to
each other in the paper are as usual in the same group; this gives five groups with four
samples in each. The groups are situated in the four corners and in the middle of the
paper. The differences were in sample group A and B which involves samples A1, A2,
B1, B2 in group A and samples A4, A5, B4 and B5 in group B.
27
A1 B1 G1 H1
A2 B2 G2 H2
D3 E3
A4 B4 D4 E4 G4 H4
A5 B5 G5 H5
Figure 30: Different sample positions on paper towel before analysis with FT-IR.
Figure 31: PCA plot of paper towel samples analyzed with FT-IR. Spectral range: 4000-500cm-1
. Both
front and backside measured. Sample A5F (frontside of sample A5) and B4B (backside of sample B4) were
removed because of suspected outliers.
28
Figure 32: PCA plot of paper towel samples analyzed with FT-IR. Spectral range; 4000-500cm-1
. Only
frontside measured.
Figure 33: PCA plot of paper towel samples analyzed with FT-IR. Spectral range: 4000-500cm-1
. Only
backside measured. Sample B4 removed because of suspected outlier.
29
The FT-IR spectrum of sample A1 is seen in figure 34. The spectra range covers from
5000-500 cm-1
.
Figure 34: FT-IR spectrum of sample A1. Frontside of the paper towel illustrated.
4.2.3 ICP-AES
The nineteen samples chosen for ICP analysis were selected from the four corners and
approximately from the middle of the sheet, to be able to view if there are similarities
between samples taken near each other, see figure 35. As the results imply the samples
taken closer to each other in the paper towel sheet are spread from each other in the PCA
score plot, see figure 36. Same sample were analyzed several times to make sure that
same results were obtained everytime. Figure 37 shows the contribution from different
elements in the loading plot.
ANOVA calculations show significant difference only for Ce. The element was
discriminative for the middle samples on the sheet. The concentration of Ce in every
sample is illustrated in figure 38. Elemental concentrations calculated into µg/g per
sample are seen in appendix A.
A1 B1 F1 G1
A2 B2 F2 G2
D3
A4 B4 D4 E4 F4 G4
A5 B5 F5 G5
Figure 35: Figure showing the paper towel samples approximate location before ICP-AES analysis.
A1 spectrum frontside
0,00
10,00
20,00
30,00
40,00
50,00
60,00
70,00
80,00
90,00
0 1000 2000 3000 4000 5000 6000
Wavenumber cm-1
Refl
ecte
d i
nte
nsit
y
30
Figure 36: PCA score plot of paper towel analysis with ICP-AES. Sample G2 was excluded because of
suspected outlier. Samples recalculated to µg/g (µg element per g of paper).
Figure 37: PCA loading plot of paper towel analysis with ICP-AES. Showing the elemental contribution to
the scores in figure 39.
31
Ce elemental concentration
0
0.5
1
1.5
2
2.5
3
A1 A2 A4 A5 B1 B2 B4 B5 D3 D4 E4 F1 F2 F4 F5 G1 G2 G4 G5
Sample
Co
nc
en
tra
tio
n (
µg
/g)
Ce 448,691
Figure 38: Concentration (µg/g) of Ce in paper towel samples analyzed with ICP-AES.
4.2.4 ICP-MS
The sectioning of paper towel samples analyzed with ICP-MS are the same as for ICP-
AES. ANOVA calculations done for the samples showed significant difference in the
elements: Ce, Sc, La, Mn, V, and Cu. The differences were between the lower left corner
(sample A4, A5, B4, and B5) and the rest of samples. PCA score plot done for all
samples also shows this difference, see figure 39. Sample A4, A5, B4, and B5 are
separated from all other samples in the score plot. Sample D3 is a possible outlier. The
elemental positions are plotted in figure 40.
The concentrations of the discriminative elements in each paper sample are shown in
figure 41-42. The concentrations of Mn and Cu are low in sample A4, A5, B4, B5 and D3
while the concentrations of Ce, Sc, La and V are high in these samples. Elemental
concentrations for all samples are shown in appendix A.
32
Figure 39: PCA score plot of paper towel analysis with ICP-MS. Samples recalculated to µg/g (µg element
per g of paper).
Figure 40: The PCA loading plot shows the elemental positions contributing to differences between
samples.
33
Elemental concentrations
-2000
0
2000
4000
6000
8000
10000
A1 A2 B1 B2 A4 A5 B4 B5 D3 D4 E4 F1 F2 G1 G2 F4 F5 G4 G5
Sample
Co
ncen
trati
on
(n
g/g
)Mn Cu
Figure 41: Elemental concentrations of Mn and Cu in paper towel analyzed with ICP-MS.
Elemental concentrations
0
1000
2000
3000
4000
5000
A1 A2 B1 B2 A4 A5 B4 B5 D3 D4 E4 F1 F2 G1 G2 F4 F5 G4 G5
Sample
Co
ncen
trati
on
(n
g/g
)
Ce Sc La V
Figure 42: Elemental concentration of Ce, Sc, La and V in paper towel analyzed with ICP-MS.
34
4.2.5 Spectrofluorometer
The paper towel samples analyzed with spectrofluorometer were taken from the four
corners and the middle of the sheet, see figure 43 below.
A1 B1 G1 H1
A2 B2 G2 H2
D3 E3
A4 B4 D4 E4 G4 H4
A5 B5 G5 H5
Figure 43: Division of paper towel samples for spectrofluorometer. Showing five groups with four samples
in each.
The score results are shown in figure 44. Samples positioned near each other in the sheet
are spread from each other in the score plot, but there tend to be a vague pattern between
samples taken from row 1-3 and with 4-5 exceptions for the samples E3, B4 and D4. The
statistic ANOVA calculations done for the principal components showed no significant
difference between the samples. A spectrum for sample A1 is shown in figure 45. The
fluorescence peak is small due to the low content of optical brighteners in paper towel.
Figure 44: Score plot for paper towel samples analyzed with spectrofluorometer.
35
A1 spectrum
0
50
100
150
200
250
300
350
400
200 300 400 500 600 700 800
Wavelenght (nm)
Flu
ore
sc
en
ce
em
iss
ion
(in
ten
sit
y)
Figure 45: Paper towel sample A1 spectrum analyzed with spectrofluorometer.
4.3 Corrugated paperboard
4.3.1 NIR analysis
The whole corrugated paperboard was cut and the samples were labeled according to
their positions on the paperboard, see figure 46. All pieces were cut because the sample
holder in NIR is much larger than the other techniques used. Figure 47 shows that the
samples are distributed with no similarities between samples from the same column or
row. This visually determined result is also statistically proven with one-way ANOVA
for each principal component and general linear model ANOVA for all the original
variables from NIR analysis. Both models prove no significant difference between
samples at different distances in the paper.
On occasion, the NIR instrument drifts; therefore same sample measured four times at the
beginning of the analysis was reanalyzed four times in the end of analysis to ensure same
results each time. Figure 48 demonstrates differences between these two sets of replicates
along the PC2 axis, which mean that there are drifts in NIR spectroscopy during analysis.
Figure 49 demonstrates a spectrum for corrugated paperboard. The sample used is A1.
A1 B1 C1 D1 E1 F1
A2 B2 C2 D2 E2 F2
A3 B3 C3 D3 E3 F3
A4 B4 C4 D4 E4 F4
Figure 46: Positions were all the samples were taken from corrugated paperboard before analysis with NIR
spectroscopy.
36
Figure 47: Score plot of corrugated paperboard samples analyzed with NIR spectroscopy. The samples
A3 and B4 were removed from the calculations because of suspected outliers.
Figure 48: All samples analyzed with NIR spectroscopy. Sample D2 was analyzed several times.
Replicate 1-4 were analyzed in the beginning while replicate 5-8 were analyzed at the end of analysis.
37
A1 spectrum
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
1200 1400 1600 1800 2000 2200 2400 2600
Wavelenght (nm)
Re
fle
cte
d i
nte
ns
ity
Figure 49: NIR spectrum of corrugated paperboard sample A1.
4.3.2 FT-IR analysis
Corrugated paperboard samples were only analyzed on one side because of the
inconsistency of the backside of the paper. The samples were analyzed on the smooth
surface i.e. frontside. Figure 50 shows were the samples originated from the paperboard.
The results in score plot 51-52 points out that samples taken from the left side of the
paper distinguish from the rest. Samples taken from column A and B are situated at the
right side in the chart while the rest are situated more at the left side.
This interpretation was proved with ANOVA calculations of principal component 1 and
2. The results from ANOVA confirmed significant difference for PC1 and samples from
column A and B were distinguished from the other samples.
Samples from group A (A1, A2, B1, and B2) distinguished from all the other groups
remarkably. Group B (A9, A10, B9, and B10) also distinguished from the other groups
on the right side of the sheet, it was more similar to group A.
A1 B1 M1 N1
A2 B2 M2 N2
G5 H5
G6 H6
A9 B9 M9 N9
A10 B10 M10 N10
38
Figure 50: Positions of the sample pieces on corrugated paperboard before FT-IR analysis. There were five
groups with four samples in each.
Figure 51: Corrugated paperboard samples analyzed with FT-IR at spectral range 4000-500cm-1
. With all
samples included.
Figure 52: Corrugated paperboard samples analyzed with FT-IR at spectral range 4000-500cm-1
. Samples
A1 and H5 excluded because of suspected outliers.
39
The FT-IR spectrum of sample A1 is shown in figure 53. The spectrum range covers
5000-500 cm-1
. The area from 4000-500 cm-1
is similar to the office paper and paper
towel.
A1 spectrum
0
10
20
30
40
50
60
0 1000 2000 3000 4000 5000 6000
Wavenumber cm-1
Re
fle
cte
d i
nte
ns
ity
Figure 53: FT-IR spectrum of corrugated paperboard sample A1.
4.3.3 ICP-AES
Previous attempts to find a good digestion procedure for corrugated paperboard left no
paperboard to take samples from the four corners and in the middle, as with the other
papers analyzed. After a digestion procedure for paperboard was found the samples were
picked out from the four long edges of the paperboard instead, see figure 54. Figure 55
and 57 shows that the samples taken near each other have no correlation and are spread in
the score plots. Samples within one group of paper seem to be more comparable with
samples from another group at a longer distance in the corrugated paperboard. The
loading plots for the scores are shown in figure 56 and 58.
ANOVA calculation showed that the elements discriminating the samples were Al and
Co. The main difference of Al found, was in sample N5, N6, M5 and M6. This group of
samples was significantly different from the others. While sample G9, G10, H9 and H10
were significantly different from the other groups when considering the element Co. The
elemental concentrations among the samples are shown in figure 59-60. An extended list
for all elemental concentrations is attached in appendix A.
40
G1 H1
G2 H2
A5 B5 M5 N5
A6 B6 M6 N6
G9 H9
G10 H10 Figure 54: Sample locations from corrugated paperboard, for analysis with ICP-AES.
Figure 55: PCA score plot on corrugated paperboard, analyzed with ICP-AES. The results were
recalculated to µg/g (µg element per g of paper). All samples included.
41
Figure 56: Loading plot for the elements contributing to the score positions.
Figure 57: Score plot on corrugated paperboard, analyzed with ICP-AES. The results were recalculated to
µg/g (µg element per g of paper). Sample N6 excluded because of suspected outlier.
42
Figure 58: Loading plot showing elements contributing to the score plot.
Elemental concentration
0
1000
2000
3000
4000
5000
6000
A5 A6 B5 B6G1
G2
H1
H2
G9
G10 H
9H10 N
5N6
M5
M6
Sample
Co
nc
en
tra
tio
n (
µg
/g)
Al 167,078
Figure 59: Al concentration of corrugated paperboard samples analyzed with ICP-AES.
43
Elemental concentration
3.5
3.6
3.7
3.8
3.9
4
4.1
4.2
4.3
A5
A6
B5
B6
G1
G2
H1
H2
G9
G10 H
9H10 N
5N6
M5
M6
Sample
Co
ncen
trati
on
(µ
g/g
)Co 238,892
Figure 60: Co concentration of corrugated paperboard samples analyzed with ICP-AES.
4.3.4 ICP-MS
Samples used for the ICP-AES analyses from section 4.3.3 were also measured by ICP-
MS. First the samples were diluted ten times with milli-Q water with an addition of
thallium. Thallium is used as internal standard for the samples. The distribution of the
samples is shown in figure 61. The plot shows similarities between sample A5, A5, B5,
B6 and G1, G2, H1 and H2. It also showed similarities between sample G9, G10, H9,
H10 and M5, M6, N5 and N6; sample M6 being an outlier was excluded from the
evaluation. The corresponding loading plot for the scores is shown in figure 62.
According to the ANOVA calculations the discriminating element was Mo. The
calculations showed significant difference. One group containing the samples M5, M6,
N5 and N6 was distinguished from all the other groups of samples. The concentration of
Mo for all analyzed samples is shown in figure 63. A list of elemental concentrations for
all samples is included in appendix A.
ANOVA calculated for principal component 1 and 2 didn’t prove significant difference.
Usually more differences between samples are found in the first principal components.
44
Figure 61: PCA plot for corrugated paperboard analyzed with ICP-MS. Sample M6 being an outlier was
excluded.
Figure 62: Elemental position for the scores on corrugated paperboard, analyzed with ICP-MS. Sample
was M6 excluded, because it was an outlier.
45
Elemental concentration
0
500
1000
1500
2000
2500
3000
A5 A6 B5 B6 G1
G2
G9
G10 H
1 H2
H9
H10 M
5M
6N5
N6
Sample
Co
nc
en
tra
tio
n (
ng
/g)
Mo
Figure 63: Concentration of Mo in corrugated paperboard analyzed with ICP-MS.
4.3.5 Spectrofluorometer
The sample positions on the corrugated paperboard are illustrated in figure 64. Samples
taken from the same group are not situated near each other in the PCA plot showing
dissimilarity in composition of optical brighteners, see figure 65. One can visually see
that there is no difference between groups, which also could be verified with ANOVA
calculation on the principal components 1 and 2. A spectrum of sample A1 is shown in
figure 66.
C1 D1
C2 D2
A1 B1 E1 F1
A2 B2 E2 F2
G1 H1
G2 H2 Figure 64: Sample positions of corrugated paperboard before analysis with spectrofluorometer.
46
Figure 65: PCA plot on corrugated paperboard analyzed with spectrofluorometer. Samples B1 and C2
excluded because these samples were outliers.
A1 spectrum
0
50
100
150
200
250
300
200 300 400 500 600 700 800
Wavelenght (nm)
Flu
ore
scen
ce e
mis
sio
n
(in
ten
sit
y)
Figure 66: Spectrum of corrugated paperboard sample A1, analyzed with spectrofluorometer.
47
4.3.6 Raman spectroscopy
The spectra obtained for office paper, paper towel and corrugated paperboard samples
analyzed with Raman spectroscopy gave no peaks, instead a broad band was obtained for
all the types of papers measured. The broad bands shown in the spectra can be a result of
high fluorescence disturbing the signals. Therefore different laser intensities were tested
at 100, 50, 25, 10 and 1% without any difference in result.
5. Discussion
The results from office paper analysis with NIR spectroscopy showed significant
difference between groups of samples within same sheet. The differences were along the
rows, which the paper was divided in. This means that the organic content like cellulose
and water is not homogenous through the entire paper. This difference occurs during the
manufacturing process of paper, and measuring organic content in paper with NIR is not
reliable as forensic evidence if one piece of paper is found with victim and the other with
the suspect.
The FT-IR results of office paper proved significant differences between pieces of paper
taken from one side of the paper and samples from the other end from the same paper.
This difference can be explained either by the samples being different in organic
composition or that papers analyzed in the beginning of the analysis differs significantly
from those samples analyzed later on the analysis.
ICP-AES and ICP-MS measures elemental composition and concentrations in the paper
pieces. Different elements were discriminative for certain groups of samples. One group
was statistically proven different with both techniques; this group was situated in the
lower left corner of the paper. The discriminative elements for both techniques altogether
were Na, K, Sr, Ti, V and Rb. This means that the concentration of elements differs
within same paper sheet it is not homogenous.
The results from the spectrofluorometer analysis of the office paper proved no significant
difference between samples, when ANOVA was calculated for principal component 1
and 2. The samples were randomly spread in the PCA plot. Samples taken from one area
of the sheet wasn’t more similar to each other than samples taken from another area at a
longer distance within same sheet of paper. This means that the content of optical
brighteners is homogenous throughout the paper. The PCA plot showed though that front
and backside samples were different, because the samples were divided in two sides of
the same plot. This observation however wasn’t statistically proven different, and thus
more analysis needs to be done.
The score results from paper towel analysis with NIR spectroscopy showed a distinction
between samples belonging to row 1-2 and 3-4, meaning that organic content is different
along the short side of the paper towel, this difference may occur during the
manufacturing process, but this difference wasn’t significant according ANOVA
48
calculations. The differences in the score plot can depend on the drifts of the instrument
since it was proven to drift for the corrugated paperboard samples analyzed with NIR
spectroscopy, even though the samples for the paper towels were analyzed randomly.
Analysis with FT-IR spectroscopy proved difference between samples from one side of
the paper towel and samples from the rest of the sheet. This diversity could also be seen
in the PCA score plots. The results points out that during the manufacturing process the
cellulose or other organic content seem to be distributed in a certain way in the beginning
and another way at the end of the sheet. The homogeneity of the organic content in paper
towels can’t be guaranteed. If two pieces coming from same paper towel were found at
different locations they could not be statistically proven being the same.
The discriminative element for paper towel analysis was Ce when analyzing the samples
with ICP-AES. The middle samples (sample D3, D4 and E4) had clearly higher
concentration of Ce than the other samples. This discrimination is not seen in the PCA
plots, the middle samples are spread from each other. When analyzing the samples again
with ICP-MS the discriminative elements were Ce, Sc, La, Mn, V and Cu. The samples
that were distinguished from the other samples were A4, A5, B4 and B5. The
concentration of Mn and Cu were lower in these samples while the concentration of Ce,
Sc, La and V was higher. The PCA plot could also visualize the distinction of these
samples located in lower left corner of the paper towel.
The content of optical brighteners tends to be different row wise in the paper towel when
evaluating the samples with PCA. This conclusion however could not be proved
statistically. The content of optical brighteners seems to be low to begin with because of
the dark color of the paper towel. More analysis should be done with spectrofluorometer
to reject similarities between samples taken close to each other in paper towels, in this
case the similarities were found between samples from the same row.
There was no statistical difference between samples in corrugated paperboard. The PCA
chart showed a random distribution of the samples, i.e. samples from different locations
in the paperboard tend to be similar in the PCA plot. Corrugated paperboard is
homogenous in organic content when analyzing it with NIR spectroscopy.
The organic content was not homogenous through the paperboard according to the results
from FT-IR analysis. Both the PCA plot and ANOVA calculation proved significant
difference between samples taken from two different ends of the same paperboard. This
conclusion should be made carefully because samples can show similarities between
samples being analyzed in the beginning and those which have been analyzed at the end
of analysis.
ICP-AES analysis of corrugated paperboard showed significant differences between
samples with different concentration of Al and Co. The difference was statistically
proven. PCA plots of the samples didn’t show these differences. The discriminative
element for ICP-MS analysis of the samples was Mo (statistically proven). The
concentration was higher in one group than in the others. ANOVA for principal
49
component 1 and 2 showed no significant difference. The concentrations of certain
elements are not constant throughout the paperboard and hence, the paperboard is not
homogenous.
There were no differences between groups of samples in corrugated paperboard,
considering content of optical brighteners. According to the PCA plot and ANOVA the
paperboard is homogenous in that aspect when it’s analyzed with spectrofluorometry.
Due to the darker color of the paperboard it is not likely that it contains that much
whitening agents.
Overall, sample pieces within paper towels and corrugated paperboard are
indistinguishable from each other in matter of organic content when analyzing with NIR
spectroscopy, but discrimination for the office paper in was found PC1. The samples for
each type of paper could be discriminated with regard to organic content if the FT-IR
instrument was used. Elemental concentration and composition could distinguish between
samples within the same office paper, paper towel and corrugated paperboard. These
papers are thus not homogenous all the way if elemental concentrations are measured
with ICP-AES or ICP-MS.
Fluorescence spectra for all the paper types show that the office paper has a higher
concentration of optical brighteners than paper towel and corrugated paperboard. The
fluorescence spectra for office paper show a larger peak, higher intensity than the other
two spectra. The content of whitening agent is homogenous through all papers, except for
the paper towel samples which visually showed difference between samples in rows.
Elements that are known to be discriminative within the same sheet of paper should be
excluded from forensic analysis if one wants be able to link one pieces of paper found at
crime scene with another piece of paper found with suspect, given that the evidence is
from the same sheet. The same reasoning follows for the other spectroscopic techniques
and their measuring wavelengths.
Previous research in the area has only been made for office papers and only to
differentiate papers from different vendors, batches or papers within same ream. In this
thesis the task was to establish if the same office paper sheet, paper towel and corrugated
paperboard was homogenous throughout in context of organic and elemental contents. If
the different sheets were not homogenous throughout, then the value of finding one piece
of paper at the crime scene and another piece from the same sheet with a suspect would
be useless as forensic evidence if the discriminative elements or wavelengths are not
excluded.
Office papers from different vendors, batches and reams could be distinguished by
elemental compositions, according to previous research in the area (1-2). Papers within
one ream and samples within one sheet could not be differentiated.
Different brands of office papers could also be discriminated by analysis of organic
contents with FT-IR (3-5).
50
6. Conclusion
Inhomogeneity within the same paper sheet was found; hence the possibility to link
evidence (piece of paper) from crime scene with a suspect is diminished. A way to solve
this problem is to exclude those variables (elements or wavelengths) that are known to
discriminate samples within one type of paper sheet.
7. Future aspects
The results from Raman analysis of office paper, paper towel and corrugated paperboard
had no value whatsoever. No peaks were obtained due to high fluorescence. Different
laser intensities tested gave all the same results; broad bands instead of peaks. One
change that unfortunately could not be performed was the excitation laser.
For future aspects it could be interesting to also try the 718 nm laser instead of the 514
nm laser used in this thesis.
Another aspect regarding the ability to exclude discriminative elements in the different
paper types is to analyze three papers within same batch to see if same elements are
discriminative each time.
51
Acknowledgement
I would like to thank my supervisor Jean Pettersson (assistant professor) for all the help
and support during my master degree project.
52
References
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[5] Casamassima R, Causin V, Marega C, Marigo A, Peluso G, Ripani L. 2010.
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53
[14] Miller JN, Miller JC. 2005. Statistics and chemometrics for analytical chemistry.
Fifth edition. Pearson education.
54
Appendix A
Elements measured at different wavelengths with ICP-AES. Analysis of office paper,
paper towel and corrugated paperboard samples.
Elements Wavelengths (nm)
Al 1 167.078 Al 2 396.152 Mn 1 257.611 Mn 2 259.373 Mg 1 279.553 Mg 2 280.270 Sr 1 407.771 Sr 2 421.552 Li 1 670.780 Li 2 460.289 Na 1 589.592 Na 2 588.995 K 1 766.491 K 2 404.721 Rb 1 420.185 Ca 1 396.847 Ca 2 393.366 Ba 1 455.404 Ba 2 233.527 Sc 1 361.384 Sc 2 335.373 Y 1 371.030 Y 2 324.228 Ti 1 334.941 Ti 2 336.121 V 1 292.464 V 2 311.071 Cr 1 267.716 Cr 2 205.552 Fe 1 259.941 Fe 2 238.204 Co 1 228.616 Co 2 238.892 Ni 1 231.604 Ni 2 221.648 B 1 249.773 B 2 249.677 Si 1 251.612 Si 2 152.672 P 1 177.495 P 2 178.287 Ce 1 418.660 Ce 2 448.691 Ca 3 317.933
55
Elemental concentration (µg/g) of office paper samples analyzed with ICP-AES.
Sample Al 1 Al 2 Mn 1 Mn 2 Mg 1 Mg 2 Sr 1 Sr 2
A1 206 247 3 3 802 817 50 51
B1 203 243 3 3 794 816 50 51
A2 230 279 3 3 811 831 51 52
B2 213 257 3 3 818 834 52 53
A10 204 250 3 3 793 813 49 50
B10 202 246 3 3 786 800 49 50
A11 260 252 -4 -4 912 913 51 51
B11 258 260 0 0 867 875 50 50
G5 207 254 3 3 816 820 51 51
H5 192 237 3 3 797 811 49 50
G6 201 242 3 3 805 819 50 50
H6 201 251 3 4 807 818 51 51
N1 205 250 3 3 809 823 52 52
O1 232 281 5 5 826 835 51 51
N2 204 253 3 4 809 816 51 51
O2 210 257 3 3 824 832 52 52
N10 204 247 3 3 800 810 50 50
O10 199 243 3 3 805 825 50 51
N11 205 246 3 3 827 833 51 52
O11 228 233 -2 -2 856 854 50 50
Sample Li 1 Li 2 Na 1 Na 2 K 1 K 2 Rb 1 Ca 1
A1 0 8 305 304 120 389 -3315 37692
B1 0 1 316 314 163 397 -2664 35923
A2 0 9 365 362 165 382 -2520 39121
B2 0 0 293 293 124 381 -2453 39091
A10 0 -6 305 304 116 316 -2057 37340
B10 0 0 305 305 102 324 -1640 35550
A11 0 -5 428 429 302 108 1330 146081
B11 0 2 443 446 436 261 -246 103072
G5 0 6 298 297 82 346 -1671 37405
H5 0 -2 276 275 90 301 -1066 36118
G6 0 5 275 276 88 291 -1358 40439
H6 0 -3 289 287 78 313 -1443 35726
N1 0 -5 274 273 73 322 -1245 35666
O1 0 -1 334 334 89 327 -1489 43413
N2 0 -1 279 280 73 316 -1541 36455
O2 0 3 284 283 76 337 -1749 42094
N10 0 1 276 276 72 286 -1180 38021
O10 0 5 284 283 81 318 -1701 41471
N11 0 8 279 278 71 304 -1023 44261
O11 0 9 261 263 92 73 2454 100953
56
Sample Ca 2 Ba 1 Ba 2 Sc 1 Sc 2 Y 1 Y 2 Ti 1
A1 21197 4 4 0 0 0 0 1
B1 20216 5 4 0 0 0 0 1
A2 21998 5 5 0 0 0 0 2
B2 21961 5 5 0 0 0 0 1
A10 20972 4 4 0 0 0 0 4
B10 19963 4 4 0 0 0 0 1
A11 85614 5 5 0 0 0 0 4
B11 59212 5 4 0 0 0 1 5
G5 21064 5 5 0 0 0 0 1
H5 20297 4 4 0 0 0 0 1
G6 22684 5 4 0 0 0 0 1
H6 20106 5 5 0 0 0 0 1
N1 20123 4 4 0 0 0 0 1
O1 24308 5 5 0 0 0 0 4
N2 20503 5 5 0 0 0 0 1
O2 23667 5 5 0 0 0 0 1
N10 21323 4 4 0 0 0 0 2
O10 23231 5 4 0 0 0 0 1
N11 24775 5 5 0 0 0 0 0
O11 57924 5 4 0 0 0 0 -1
Sample Ti 2 V 1 V 2 Cr 1 Cr 2 Fe 1 Fe 2 Co 1
A1 4 1 0 -2727 -363 30 30 0
B1 4 1 1 1359 534 51 50 0
A2 4 1 0 -835 -10 41 42 0
B2 4 1 0 -3038 -474 31 30 0
A10 7 1 0 -1674 -213 40 40 0
B10 4 1 0 -3013 -504 36 36 0
A11 6 1 -1 -10578 -2405 -16 -16 1
B11 7 0 -1 401 5 36 38 0
G5 3 1 0 -3909 -744 26 26 0
H5 4 1 1 -1248 -77 41 41 0
G6 4 1 1 -2065 -381 38 38 0
H6 3 1 1 -324 10 45 45 0
N1 4 1 1 -2814 -488 48 48 0
O1 6 0 0 13868 2942 109 109 0
N2 4 1 1 641 208 49 50 0
O2 4 1 0 -3054 -549 31 31 1
N10 5 1 0 -1972 -299 37 37 0
O10 4 0 0 1299 285 46 46 0
N11 3 1 0 -4282 -865 23 24 0
O11 2 1 0 -13101 -2875 -28 -26 1
57
Sample Co 2 Ni 1 Ni 2 B 1 B 2 Si 1 Si 2 P 1
A1 1 -6 -31 36 36 -1602 -1753 10
B1 1 -4 12 18 18 792 677 8
A2 1 -5 -9 113 113 -66 -122 9
B2 0 -6 -25 22 22 -1139 -1245 8
A10 0 -6 -21 34 34 -981 -1124 4
B10 0 -5 -30 25 25 -1509 -1660 6
A11 0 -22 -103 141 142 -4979 -5181 1
B11 1 -10 -11 235 236 -264 -264 9
G5 0 -7 -31 53 53 -1478 -1587 7
H5 1 -5 26 -1 -1 2393 2342 5
G6 1 -7 -6 14 14 64 -31 7
H6 1 -5 -29 35 35 -1436 -1585 5
N1 1 -6 -27 26 26 -1431 -1541 5
O1 1 6 21 103 103 1106 1088 10
N2 1 -5 -22 38 38 -1087 -1258 6
O2 1 -7 -37 56 56 -1847 -1956 7
N10 1 -6 -23 40 40 -1004 -1168 5
O10 1 -6 -18 30 30 -383 -497 7
N11 1 -8 -19 17 17 -653 -735 7
O11 0 -19 -79 53 52 -4002 -4068 7
Sample P 2 Ce 1 Ce 2 Ca 3
A1 21 0 -1 161980
B1 16 1 1 170343
A2 14 1 -2 167598
B2 14 1 1 173630
A10 18 1 0 166616
B10 19 1 1 160637
A11 20 0 3 167072
B11 8 2 -3 161770
G5 16 1 -1 165920
H5 17 0 1 166043
G6 17 1 3 163587
H6 19 1 1 163743
N1 19 2 3 169534
O1 13 0 1 167302
N2 18 1 -2 159725
O2 17 2 0 166359
N10 19 1 1 161873
O10 18 0 1 166570
N11 17 1 2 166278
O11 16 1 2 166293
58
Elemental concentration (ng/g) of office paper samples analyzed with ICP-MS.
Sample Pb Ce Sc La Mn V W
A1 46 122 -625 76 4282 564 5623
A2 79 126 -237 79 4594 584 6141
A10 46 92 -443 76 3924 534 5145
A11 -171 -114 -2335 -57 968 285 7005
B1 44 175 15 73 4440 568 5371
B2 47 1043 -269 553 4186 553 6114
B10 43 115 -533 72 3759 504 5372
B11 202 81 -1008 -40 2782 444 6815
G5 45 151 -591 76 3770 500 4921
G6 49 228 -342 81 3602 473 5362
H5 132 146 395 73 3817 483 5923
H6 72 145 -623 72 3808 478 5546
N1 72 232 -565 101 3867 507 6040
N2 74 207 -487 103 3796 487 5451
N10 77 215 -445 77 3518 445 5330
N11 53 178 -480 89 3326 445 5745
O1 52 140 -17 87 5259 507 5433
O2 51 136 -594 85 3278 425 5316
O10 50 134 -384 84 3457 451 5695
O11 -40 -79 -2016 -40 2332 277 6126
Sample Zr Rb Ti Mo Zn Cu
A1 244 15 3795 -290 914 -213
A2 758 16 5226 142 2747 189
A10 183 46 6031 -107 1191 31
A11 0 57 7346 -4613 8087 -456
B1 58 15 3945 451 1805 29
B2 253 16 4755 -427 1643 -95
B10 202 14 3816 -446 1872 -86
B11 806 40 6976 -1008 2097 565
G5 273 15 3346 -651 1151 -121
G6 0 16 3471 -342 4563 -33
H5 -88 15 4226 -44 2311 0
H6 232 14 2737 217 2375 87
N1 174 14 3056 -333 1333 -87
N2 177 15 3264 281 2688 0
N10 154 15 4439 -292 2765 31
N11 107 18 2721 -872 1387 -36
O1 454 17 5818 2184 2690 314
O2 204 17 3448 -662 849 -136
O10 100 17 3223 317 1937 -100
O11 395 -40 198 -3992 632 -791
59
Elemental concentration (µg/g) of paper towel samples analyzed with ICP-AES.
Sample Al 1 Al 2 Mn 1 Mn 2 Mg 1 Mg 2 Sr 1 Sr 2
A1 853 906 5 6 141 141 13 13
A2 991 1018 7 7 164 164 15 15
A4 871 923 5 5 149 146 14 14
A5 986 980 6 6 159 156 14 14
B1 973 982 6 7 173 159 15 15
B2 845 870 5 6 144 144 14 13
B4 963 943 6 6 162 163 14 14
B5 867 905 5 5 138 139 13 13
D3 830 866 5 5 135 138 13 13
D4 985 977 6 6 164 160 15 15
E4 922 933 5 5 147 150 14 14
F1 840 872 5 5 144 146 13 13
F2 1001 995 6 6 161 162 15 15
F4 895 930 5 5 161 151 13 13
F5 965 926 5 6 155 155 14 14
G1 994 984 6 6 159 157 15 15
G2 904 929 7 7 151 146 13 14
G4 939 915 6 6 158 160 15 14
G5 894 913 6 6 147 149 13 13
Sample Li 1 Li 2 Na 1 Na 2 K 1 K 2 Rb 1 Ca 1
A1 0 1 162 161 39 -28 1559 14622
A2 0 -3 190 189 43 28 -247 14618
A4 0 2 172 171 39 -12 917 15254
A5 0 1 177 176 39 42 -1012 17638
B1 0 -1 189 188 41 47 -763 15493
B2 0 3 173 172 40 -23 1405 15939
B4 0 1 199 199 39 93 -2456 16539
B5 0 1 167 166 38 -11 1166 16448
D3 0 1 148 146 36 -5 618 16709
D4 0 2 200 199 41 28 -440 17456
E4 0 1 167 166 41 -22 1293 18224
F1 0 2 153 152 42 -34 1871 18624
F2 0 1 185 184 40 39 -469 18035
F4 0 -2 167 166 39 -16 1332 16843
F5 0 2 175 174 38 31 -713 20061
G1 0 5 178 177 39 35 -687 17641
G2 0 2 170 169 49 -19 1283 17498
G4 0 1 170 168 38 39 -288 17989
G5 0 1 208 206 40 -23 1420 20081
60
Sample Ca 2 Ba 1 Ba 2 Sc 1 Sc 2 Y 1 Y 2 Ti 1
A1 8871 3 3 0 0 0 0 18
A2 8842 4 4 0 0 0 0 12
A4 9253 3 3 0 0 0 0 12
A5 10719 3 3 0 0 0 0 18
B1 9387 3 3 0 0 0 0 12
B2 9693 3 3 0 0 0 0 6
B4 10029 3 3 0 0 0 0 11
B5 10010 4 4 0 0 0 0 12
D3 10195 3 3 0 0 0 0 14
D4 10605 4 4 0 0 0 0 12
E4 11050 3 3 0 0 0 0 14
F1 11347 3 4 0 0 0 0 13
F2 10976 3 4 0 0 0 0 13
F4 10248 4 4 0 0 0 0 17
F5 12211 3 3 0 0 0 0 13
G1 10728 3 3 0 0 0 0 11
G2 10635 5 5 0 0 0 0 17
G4 10947 3 3 0 0 0 0 13
G5 12231 3 3 0 0 0 0 16
Sample Ti 2 V 1 V 2 Cr 1 Cr 2 Fe 1 Fe 2 Co 1
A1 16 0 1 -9 -8 29 27 0
A2 11 0 1 -5 -3 41 40 0
A4 11 0 0 -9 -8 22 22 0
A5 16 0 1 -10 -8 22 20 0
B1 11 0 1 -6 -4 44 44 0
B2 5 0 0 -9 -8 25 24 0
B4 10 0 0 -8 -7 34 33 0
B5 11 0 0 -9 -8 25 22 0
D3 12 0 0 -9 -8 28 26 0
D4 10 0 0 -9 -7 29 29 0
E4 13 0 0 -9 -7 33 31 0
F1 12 0 0 -10 -9 22 21 0
F2 12 0 0 -10 -8 21 21 0
F4 15 0 1 -10 -9 20 20 0
F5 11 0 1 -9 -7 28 28 0
G1 10 0 0 -10 -7 22 20 0
G2 15 0 1 6 6 87 86 0
G4 11 0 1 -10 -8 18 17 0
G5 14 0 1 -3 -1 46 43 0
61
Sample Co 2 Ni 1 Ni 2 B 1 B 2 Si 1 Si 2 P 1
A1 0 -7 -5 -5 -5 215 106 16
A2 0 -6 3 -8 -8 603 625 16
A4 0 -7 0 -1 -1 421 418 17
A5 0 -9 -7 4 4 335 237 14
B1 0 -6 0 -6 -6 506 487 16
B2 0 -7 -4 -5 -5 259 161 15
B4 0 -8 -7 22 22 279 388 25
B5 0 -8 -6 -6 -6 282 190 16
D3 0 -7 -1 -3 -3 308 247 16
D4 0 -8 -2 -15 -15 497 439 13
E4 0 -9 -3 -4 -4 290 213 15
F1 0 -9 -6 -11 -11 268 162 16
F2 0 -9 -4 2 2 405 316 14
F4 0 -9 -5 -8 -8 293 243 16
F5 0 -9 -6 -7 -7 271 126 14
G1 0 -9 -1 4 4 373 256 16
G2 1 3 6 -4 -4 236 114 15
G4 0 -8 -2 -11 -11 470 421 12
G5 0 -4 -1 6 6 193 29 16
Sample P 2 Ce 1 Ce 2 Ca 3
A1 39 1 0 4844
A2 51 1 1 6030
A4 41 1 1 5106
A5 45 1 0 5657
B1 51 1 1 5645
B2 44 1 2 5314
B4 30 1 1 5576
B5 39 1 2 4742
D3 37 1 2 4513
D4 51 2 3 5675
E4 42 1 2 5149
F1 42 1 1 4971
F2 54 1 1 5879
F4 36 1 1 5091
F5 48 1 1 5376
G1 55 1 1 5844
G2 43 1 1 5155
G4 47 2 1 5698
G5 41 1 1 4726
62
Elemental concentration (ng/g) of paper towel samples analyzed with ICP-MS.
Sample Pb Ce Sc La Mn V W
A1 299 705 148 312 6192 338 132
A2 626 706 200 326 6958 365 67
B1 386 1493 1936 1090 354 2580 2058
B2 298 739 146 326 6144 312 201
A4 380 1061 3668 1886 1976 2266 803
A5 596 1538 3140 768 1969 4498 457
B4 2178 1886 593 2192 1113 1133 3078
B5 847 1580 2927 2615 1982 2440 134
D3 2342 2345 3267 875 -806 4107 1470
D4 404 748 176 326 6205 342 49
E4 679 827 199 323 5827 355 261
F1 330 762 187 313 6148 330 102
F2 305 756 86 321 6009 321 35
G1 267 646 256 298 5389 314 316
G2 326 749 130 326 8061 373 80
F4 314 691 170 314 5771 344 62
F5 320 679 184 302 5577 338 39
G4 304 658 181 288 5844 336 3
G5 515 713 148 319 6471 373 92
Sample Zr Rb Ti Mo Zn Cu
A1 3224 299 19240 -1020 2033 6355
A2 3020 287 12608 -409 1150 6534
B1 1955 2884 -3063 64 2072 1727
B2 2758 283 4916 -869 3516 6506
A4 3008 5414 4858 -615 2038 2241
A5 2690 2648 -3479 3364 2197 708
B4 2232 1162 -471 363 297 1400
B5 6161 1593 6692 2114 1902 1098
D3 6993 3395 6197 69 447 2300
D4 3162 279 10744 -952 2316 6803
E4 3387 291 14330 -555 3525 6901
F1 3508 280 13444 -1012 2807 6651
F2 2850 289 850 -1192 1703 6899
G1 3194 251 13768 -899 1273 6056
G2 3336 311 15713 2062 2474 6841
F4 3422 284 16564 -1097 3055 6063
F5 3266 302 15185 -948 2066 6137
G4 3047 272 8998 -1140 1553 6427
G5 3437 284 17090 -875 3428 6143
63
Elemental concentration (µg/g) of corrugated paperboard samples analyzed with ICP-
AES.
Sample Al 1 Al 2 Mn 1 Mn 2 Mg 1 Mg 2 Sr 1 Sr 2
A5 4144 6965 28 30 706 714 47 47
A6 4224 7156 27 29 721 720 46 47
B5 4064 6527 27 29 699 700 46 47
B6 4254 6760 28 31 718 722 48 48
G1 4223 6764 27 29 682 686 45 45
G2 4179 6532 27 29 710 711 48 48
H1 4278 6954 26 28 692 691 44 45
H2 4303 7131 28 30 677 693 46 46
G9 4351 7080 28 30 698 703 47 47
G10 3908 6376 26 28 686 689 46 47
H9 3779 5811 28 30 697 699 47 47
H10 3840 7167 27 30 678 690 46 47
N5 4805 6807 28 30 719 721 47 48
N6 4726 6792 26 28 728 722 40 41
M5 4455 6738 28 30 737 727 49 49
M6 4389 6822 27 28 683 689 45 45
Sample Li 1 Li 2 Na 1 Na 2 K 1 K 2 Rb 1 Ca 1
A5 6 6 398 401 364 216 846 28212
A6 6 12 311 313 348 169 585 30601
B5 5 6 400 403 309 224 791 32340
B6 6 8 370 372 338 214 863 34185
G1 5 9 321 324 347 226 1044 33518
G2 5 13 333 336 294 212 884 35829
H1 5 12 293 296 331 200 783 33026
H2 5 12 353 356 369 234 840 32529
G9 6 7 323 325 358 229 902 32613
G10 5 10 284 286 283 80 85 33151
H9 5 8 359 362 263 216 854 33211
H10 6 9 382 378 391 207 751 22609
N5 5 25 334 334 320 228 1040 52753
N6 5 7 227 228 288 67 79 57810
M5 5 6 338 340 305 224 913 44582
M6 5 9 301 303 333 224 819 38441
64
Sample Ca 2 Ba 1 Ba 2 Sc 1 Sc 2 Y 1 Y 2 Ti 1
A5 16036 37 36 0 0 1 2 220
A6 17560 37 35 0 0 1 3 297
B5 18594 36 35 0 0 1 2 218
B6 19647 36 35 0 0 1 2 208
G1 19283 36 34 0 0 1 3 243
G2 20709 38 37 0 0 1 3 221
H1 19018 38 37 0 0 1 3 293
H2 18731 35 33 0 0 1 3 241
G9 18744 35 34 0 0 1 3 239
G10 19184 36 34 0 0 1 3 259
H9 19120 40 39 0 0 1 2 192
H10 12756 39 37 0 0 1 3 222
N5 31086 37 36 0 0 1 3 230
N6 34601 36 35 0 0 1 3 290
M5 25989 40 39 0 0 1 2 226
M6 22167 36 35 0 0 1 3 232
Sample Ti 2 V 1 V 2 Cr 1 Cr 2 Fe 1 Fe 2 Co 1
A5 187 3 5 6 10 511 506 1
A6 257 3 7 6 9 509 504 1
B5 189 3 5 6 9 482 478 1
B6 179 3 5 6 10 513 515 1
G1 204 3 6 7 10 495 496 1
G2 191 3 6 6 9 485 488 1
H1 252 2 6 5 8 479 476 1
H2 208 3 6 6 10 494 495 1
G9 202 3 6 6 10 508 507 1
G10 220 2 6 5 7 462 461 1
H9 166 2 5 6 9 473 478 1
H10 192 3 6 6 9 499 500 1
N5 199 3 5 6 11 519 520 1
N6 250 3 6 5 8 501 493 1
M5 196 3 6 7 10 519 512 1
M6 201 3 6 6 9 494 487 1
65
Sample Co 2 Ni 1 Ni 2 B 1 B 2 Si 1 Si 2 P 1
A5 4 0 1 44 44 129 129 125
A6 4 -1 8 24 24 610 631 124
B5 4 0 2 39 39 147 145 121
B6 4 -1 0 33 33 114 104 122
G1 4 -1 4 32 32 322 335 118
G2 4 -1 0 29 29 109 93 123
H1 4 0 9 22 22 652 706 116
H2 4 66 69 40 40 123 127 124
G9 4 -1 0 30 30 102 95 121
G10 4 1 10 19 19 594 630 117
H9 4 -1 1 30 30 123 111 122
H10 4 0 1 39 39 90 88 126
N5 4 -2 0 35 35 169 165 121
N6 4 -3 7 17 17 820 865 117
M5 4 -2 0 31 31 117 92 123
M6 4 -1 0 31 31 97 99 115
Sample P 2 Ce 1 Ce 2 Ca 3
A5 130 4 3 30014
A6 156 5 5 28193
B5 135 4 4 27818
B6 121 4 3 29518
G1 122 4 4 27220
G2 137 4 4 30423
H1 148 4 2 27569
H2 122 5 2 28174
G9 125 4 4 27455
G10 131 4 2 28861
H9 132 4 4 29666
H10 118 4 5 27949
N5 125 3 2 29000
N6 157 4 1 21575
M5 151 4 3 29790
M6 100 3 3 26181
66
Elemental concentration (ng/g) of corrugated paperboard samples analyzed with ICP-MS.
Sample Pb Ce Sc La Mn V W
A5 9076 3351 1100 1808 42458 3986 1460
A6 8135 3259 953 1747 38756 3762 1370
B5 9003 3438 1173 1787 41230 3969 1641
B6 10286 3252 1092 1683 40935 3694 1439
G1 8675 3354 1011 1762 40503 3932 1321
G2 8728 3404 1020 1762 40998 3775 1476
G9 8824 3439 1070 1830 46305 4425 1514
G10 8660 3498 1060 1804 40307 3784 1597
H1 7730 3251 883 1737 38834 3678 1474
H2 9058 3291 1011 1742 41621 3768 1342
H9 8558 3302 1147 1778 41773 3962 1513
H10 7731 3332 1103 1773 41539 4081 1433
M5 9328 3531 1188 1878 44856 4336 1562
M6 9070 3574 1256 1884 46621 4533 1810
N5 9469 3307 1040 1764 42392 3895 1391
N6 7308 3257 985 1773 40208 3897 1416
Sample Zr Rb Ti Mo Zn Cu
A5 17928 4066 422816 1909 26567 45390
A6 17167 4033 428134 1750 27952 41535
B5 19287 3669 455363 1763 32008 46844
B6 19329 3850 405918 1746 22060 41880
G1 18618 4083 425070 1794 22358 41976
G2 17576 3597 416439 1580 26123 45140
G9 18518 4206 470901 2115 33964 48051
G10 18118 3705 423950 1779 33026 44231
H1 16624 4084 418469 1570 26493 42223
H2 18252 4253 421657 1830 0 44515
H9 19465 3367 448188 2040 25158 44785
H10 18237 4224 452727 1874 28531 43805
M5 19915 3809 461200 2074 30819 49072
M6 19028 4277 469213 2748 39109 52505
N5 20424 4404 409173 2448 24608 45467
N6 16678 3909 443589 1878 21775 42670