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

[1] McGaw EA, Smith RW, Szymanski DW. 2009. Determination of trace elemental

concentrations in document papers for forensic comparison using inductively coupled

plasma mass spectrometry. J. forensic sci, 54: 1163-1170.

[2] Baker AT, Byrne JP, Spence LD. 2000. Characterization of document paper using

elemental compositions determined by inductively coupled plasma mass spectrometry.

J. Anal. At. spectrum 15: 813-819

[3] Kher A, Maynard P, Mulholland M, Reedy B. 2001. Classification of document

papers by infrared spectroscopy and multivariate statistical techniques. Applied

spectroscopy 55: 1192-1198.

[4] Manso M, Carvalho ML. 2009. Application of spectroscopic techniques for the

study of paper documents: A survey. Spectrochimica Acta part B64: 482-490

[5] Casamassima R, Causin V, Marega C, Marigo A, Peluso G, Ripani L. 2010.

Forensic differentiation of paper by X-ray diffraction and infrared spectroscopy.

Forensic science international 197 : 70-74

[6] Punsuwan V, Terdwongworakul A, Thanapse W, Tsuchikawa S. 2005. Rapid

assessment of wood chemical properties and pulp yield of eucalyptus camaldulensis in

Thailand tree plantations by near infrared spectroscopy for improving wood selection

for high quality pulp. J. Wood sci 51: 167-171.

[7] Huang A, Li G, Shan Y, Zhang Z, Zhu X. 2009. Prediction of wood property in

Chinese fir based on visible/near-infrared spectroscopy and least square-support vector

machine. Spectrochimica Acta part A74: 344-348.

[8] Bundy WM, Ishley JN. 1991. Kaolin in paper filling and coating. Applied clay

science 5: 397-420.

[9] Eriksson K-E. 1996. Papperstillverkning. SUM AB.

[10] Harris DC. 2007. Quantitative chemical analysis. Seventh edition. W.H. Freeman

and company

[11] Thomas R.2004. Practical guide to ICP-MS. Marcel Dekker, Inc

[12] Larkin PJ. 2011. IR and Raman spectroscopy, principles and spectral

interpretation. Elsevier Inc

[13] Bell S. 2006. Forensic chemistry. Pearson Prentice Hall.

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