Characterisation of Soils with the use of Instrumental ... · ABSTRACT The value of soil evidence...

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Techniques: A Multivariate Techniques: A Multivariate Techniques: A Multivariate Techniques: A Multivariate

Forensic Study Forensic Study Forensic Study Forensic Study By

Katrina Leigh Onions

Bachelor Applied Science (Chemistry)

This thesis is submitted in partial fulfilment

of the requirements for the degree of

Master of Applied Science

School of Physical and Chemical Science

Queensland University of Technology

2009

STATEMENT OF ORIGINAL AUTHORSHIP

The work submitted in this thesis has not been previously submitted for a degree

or diploma at any other tertiary education institution. To the best of my knowledge and

belief, the information contained in this thesis contains no material previously published

or written by any other person except where due reference is made.

_________________________ ______________________

Katrina Onions Date

ACKNOWLEDGEMENTS

Firstly, I would like to express my sincere thanks to my supervisor, Dr. Serge

Kokot for his continued advice, assistance and guidance over the duration of this

research. Thank-you also to Associate Professor Ray Frost, my associate supervisor, for

financial support enabling me to attend the 13th ICNIRS conference.

I express my gratitude to Dr. Andy Hammond, School of Natural Resources, for

his insight into soils, their formation, configuration and characterisation. Thanks must

also be acknowledged for Dr. Les Dawes, Faculty of Built Environment and Engineering,

for supplying the soils used in this study, and, Dr. Llew Rintoul, School of Physical and

Chemical Sciences, for all of his help with various aspects of vibrational spectroscopy

and the operation of the instruments used in this investigation. Thank-you to Bill Kwecian

from the School of Natural Resource Science for his assistance with operating,

interpreting and obtaining ICP results. Thank-you to Tony Raftery for the much

appreciated assistance with operation and interpretation of the XRD results. I must also

express my gratitude to Dr. Theo Kloprogge for help with NIR spectral interpretation.

There was no end to the constant assistance and advice displayed by all of the previously

mentioned QUT staff members.

An expression of thanks also goes to the support staff of the School of Physical

and Chemical Sciences, Queensland University of Technology, for their assistance with

organisational formalities and financial requirements. I would like to express my sincere

appreciation to Astra Panel Pty. Ltd. for bursary funding and financial support over the

duration of this project. I would like to show my appreciation to the Royal Australian

Chemical Institute (RACI) along with the Inorganic Materials Research Program for

financial support which enabled me to attend conferences related to my topic. I would like

to thank my fellow undergraduate and postgraduate students, past and present, for their

guidance and support during my time at Queensland University of Technology.

Finally, a very special thank-you to my parents for their love, encouragement and

financial assistance especially over the past 5 years. Thank-you to Ian for listening and

being understanding of my highs and lows that were experienced during the completion

of this work.

ABSTRACT

The value of soil evidence in the forensic discipline is well known. However, it

would be advantageous if an in-situ method was available that could record responses

from tyre or shoe impressions in ground soil at the crime scene. The development of

optical fibres and emerging portable NIR instruments has unveiled a potential

methodology which could permit such a proposal.

The NIR spectral region contains rich chemical information in the form of overtone

and combination bands of the fundamental infrared absorptions and low-energy electronic

transitions. This region has in the past, been perceived as being too complex for

interpretation and consequently was scarcely utilized. The application of NIR in the

forensic discipline is virtually non-existent creating a vacancy for research in this area.

NIR spectroscopy has great potential in the forensic discipline as it is simple, non-

destructive and capable of rapidly providing information relating to chemical composition.

The objective of this study is to investigate the ability of NIR spectroscopy

combined with Chemometrics to discriminate between individual soils. A further objective

is to apply the NIR process to a simulated forensic scenario where soil transfer occurs.

NIR spectra were recorded from twenty-seven soils sampled from the Logan region in

South-East Queensland, Australia. A series of three high quartz soils were mixed with

three different kaolinites in varying ratios and NIR spectra collected. Spectra were also

collected from six soils as the temperature of the soils was ramped from room

temperature up to 6000C. Finally, a forensic scenario was simulated where the transferral

of ground soil to shoe soles was investigated.

Chemometrics methods such as the commonly known Principal Component

Analysis (PCA), the less well known fuzzy clustering (FC) and ranking by means of multi-

criteria decision making (MCDM) methodology were employed to interpret the spectral

results. All soils were characterised using Inductively Coupled Plasma Optical Emission

Spectroscopy and X-Ray Diffractometry.

Results were promising revealing NIR combined with Chemometrics is capable of

discriminating between the various soils. Peak assignments were established by

comparing the spectra of known minerals with the spectra collected from the soil

samples. The temperature dependent NIR analysis confirmed the assignments of the

absorptions due to adsorbed and molecular bound water. The relative intensities of the

identified NIR absorptions reflected the quantitative XRD and ICP characterisation

results. PCA and FC analysis of the raw soils in the initial NIR investigation revealed that

the soils were primarily distinguished on the basis of their relative quartz and kaolinte

contents, and to a lesser extent on the horizon from which they originated. Furthermore,

PCA could distinguish between the three kaolinites used in the study, suggesting that the

NIR spectral region was sensitive enough to contain information describing variation

within kaolinite itself.

The forensic scenario simulation PCA successfully discriminated between the

‘Backyard Soil’ and ‘Melcann® Sand’, as well as the two sampling methods employed.

Further PCA exploration revealed that it was possible to distinguish between the various

shoes used in the simulation. In addition, it was possible to establish association between

specific sampling sites on the shoe with the corresponding site remaining in the

impression. The forensic application revealed some limitations of the process relating to

moisture content and homogeneity of the soil. These limitations can both be overcome

by simple sampling practices and maintaining the original integrity of the soil. The results

from the forensic scenario simulation proved that the concept shows great promise in the

forensic discipline.

Keywords: Soil, NIR, XRD, ICP, Forensics, Chemometrics, MCDM.

TABLE OF CONTENTS

Statement of Original Authorship ……………………………………………………………… i

Acknowledgements …………………………………...….…………………………………….. ii

Abstract ………………………………………………………………………………………….. iii

Table of Contents …………………………………………………………………………….…. v

List of Figures ……………………………………………………...…………………………... viii

List of Tables ………………………………………………………………………………….. . xii

Abbreviations …………………………………………………………………………………... xiii

1.0 Introduction …………………………………………………………...…………..1

1.1 Prologue to the Investigation ……………………………………………….…………..1

1.2 The Development of Soil Evidence in Forensic Science ……….………..………… 4

1.3 The Five Soil Forming Factors ………………………………...…..…….…………… 5

1.3.1 Climate ……………………………………………………………….…………. 5

1.3.2 Topography ………………………………………………………….…………. 5

1.3.3 Biota …………………………………………………………………….…….… 6

1.3.4 Time …………………………………………………………………………….. 6

1.3.5 Parent Material ……………………………………………………...…………. 6

1.4 Soil Profiles & Horizons ………………………………………………………………... 8

1.5 Soil Classification ………………………………………………………...…………... 10

1.6 Soil Sampling in Forensic Science ……………………………………….…………. 10

1.6.1 Factors Governing the Collection of Forensic Soil Samples …………..… 10

1.6.2 Methods for Collecting Forensic Soil Samples ……..…………………….. 11

1.6.3 Soil Sampling Sites …………………………………………………………... 14

1.7 Soil Pre-treatments …………………………...……………………….……………… 15

1.8 Current Methods of Forensic Soil Analysis ……………………......………………. 16

1.8.1 Common Forensic Methods …………………………..…………………….. 17

1.8.2 Instrumental Methods ………………………………………………...……… 19

1.9 Vibrational Spectroscopy .……………………………………………………………. 21

1.9.1 Infrared (IR) Spectroscopy …………………………………………….. …... 21

1.9.2 Fourier Transform Infrared (FT-IR) Spectroscopy ..………………………. 23

1.9.3 Near Infrared (NIR) Spectroscopy …………………………………….……. 24

1.9.4 Diffuse Reflectance Infrared Fourier Transform (DRIFT) Spectroscopy .. 26

1.10 Inductively Coupled Plasma – Optical Emission Spectroscopy …….………...…. 27

1.10.1 ICP Instrumentation …………………………..……………………………… 27

1.10.2 ICP Spectral Interferences …………………...…………………………..…. 29

1.11 X-Ray Diffractometry ………………….………………………………..…………….. 29

1.11.1 XRD Concept …………….…………………….…………….….…………… 29

1.11.2 XRD Instrumentation ………………………………………………………… 30

2.0 Analytical Procedures …………………………………..………………….. 37

2.1 Collection and Preparation of Soils …………………………………....…………… 37

2.2 Materials and Methodology for X-ray Diffraction Analysis ………………....…….. 37

2.2.1 Air Dried and Glycolated Thin Film Preparation ………….…………….… 38

2.2.2 Powder Preparation ………………………………………………………….. 39

2.2.3 XRD Instrument Parameters …………………………………...…………… 39

2.3 Materials and Methodology for Inductively Coupled Plasma Analysis ……..…… 40

2.3.1 Lithium Metaborate Fusion ………………………………..………………… 40

2.3.2 ICP Instrument Parameters ………………………………………..……….. 41

2.4 Materials and Methodology for the Initial Investigation of NIR Spectroscopy for the

Discrimination of Soils ………………………………...……………………………… 42

2.5 Materials and Methodology for Quartz-Kaolinite Comparison by NIR and

Chemometrics ..……………………………….…………………………….………… 43

2.6 Materials and Methodology for Temperature Dependent NIR Analysis ….……... 44

2.7 Materials and Methodology for the Simulation of a Possible Forensic Application

…………………………………………………………………………………………… 46

2.7.1 Dry Brushed Method …………………………………………………….…… 46

2.7.2 Wet Sampled, Oven Dried Method ………………………………………… 47

2.8 Chemometrics and Multi-Criteria Decision Making Theory and Techniques …… 49

2.8.1 Pre-treatment Methods for the Raw Data Matrix ………………….……… 50

2.8.2 Principal Component Analysis (PCA) ……………………………………… 51

2.8.3 Fuzzy Cluster (FC) Analysis ………………………………………………… 52

2.8.4 PROMETHEE and GAIA ……………………………….…………………… 52

3.0 Results and Discussion: Characterisation of Soils …………….…….. 57

3.1 X-Ray Diffraction Analysis .……………..…..…………………..…………………… 57

3.2 Inductively Coupled Plasma Analysis ….………………………..………………….. 59

3.3 Initial NIR Analysis ……………………………………………………………………. 63

3.3.1 Raw Spectra and Observations …………………………………………….. 63

3.3.2 Peak Assignments ……………………….…………………………...……… 65

3.3.3 Comparison of Soil Spectra with Mineral Spectra …………………..……. 67

3.3.4 Second Derivative Spectra …………….……………………………….…… 73

3.4 NIR Analysis: Quartz-Kaolinite Mixtures …………………..…...…….…………….. 76

3.5 NIR Analysis: Temperature Dependent ……………….…………………....……… 78

4.0 Results & Discussion:Chemometrics & Multivariate Data Analysis ... 83

4.1 Pre-treatment of Data Matrices ……………………….…………………………..… 83

4.2 Raw Soils: Initial NIR Investigation ……………………….………………………… 83

4.2.1 Principal Component Analysis …………………………………………….... 83

4.2.2 Fuzzy Cluster (FC) Analysis ………………………………………………… 85

4.2.3 Loadings Plots ………………………………………………………………... 90

4.3 Quartz-Kaolinite Mixtures ………………………………………………………….… 92

4.3.1 Preliminary Principal Component Analysis …………………….………….. 92

4.3.2 Principal Component Analysis Using One Soil with One Kaolinite .......... 94

4.3.3 PROMETHEE and GAIA ……………………………………………….…… 98

4.4 Temperature Dependent NIR Analysis ……………………………………………. 100

5.0 Results & Discussion: Application of NIR to Forensic Scenario ….... 104

5.1 The Scenario ……………………………………………………………………….... 104

5.2 Pre-treatment of Data Matrices ……………………………………………………. 104

5.3 Principal Component Analysis ……………………………………………………... 106

5.3.1 Complete Data Matrix: 3 Shoes, 2 Sampling Methods and 2 Soils …… 106

5.3.2 Separation Based on Sampling Method ……………………………….… 108

5.3.3 Separation Based on Shoe Type Using Dry Brushed Method ……….... 108

5.3.4 Separation Based on Shoe Type Using Wet Sampled Method ……….. 111

5.3.5 Discussion of the Separation Based on Shoe Type …………………….. 111

5.3.6 Proposed Triboelectric Effect Hypothesis ………………………………... 113

5.3.7 Site Specific Correlation ………………………………………………….... 117

6.0 Concluding Remarks & Future Work …………………………………..… 121

6.1 Concluding Remarks …………………………………………………...…………… 121

6.1.1 Initial Investigation ………………………………………………..………… 121

6.1.2 Application to Forensic Scenario …………………………………..……… 122

6.1.3 Summary ……………………………………………………………..……… 122

6.2 Future Work ……………………………………………………………………..…… 123

7.0 Appendix……………………………………………………………………… 125

LIST OF FIGURES

Figure 1.1 – Schematic diagram illustrating the horizontal distinctions in a soil profile.

Figure 1.2 – Interpretation of Cunningham’s proposed method of sampling soil from tyre

and shoe impressions.

Figure 1.3 – The wave nature of plane polarised electromagnetic radiation.

Figure 1.4 – Examples of vibrational modes for a methylene group.

Figure 1.5 – NIR Fibre Optic Probe, Smart Near-IR FibrePort.

Figure 2.1 – Soil 4870-26 ‘Backyard Soil’ and 4870-27 ‘Melcann® Sand’ in blue tidy tray

containers used for the forensic scenario simulation.

Figure 2.2 – The three shoes used in the forensic scenario simulation a.) Walk Shoe, b.)

Jogger, and c.) Leather Shoe.

Figure 2.3 – Impression remaining on the surface of the soil from contact of walk shoe

with the soil (15cm metal ruler included for scale).

Figure 2.4 – Wet soil adhering to the walk shoe sole after contact with the soil.

Figure 3.1 – Raw NIR spectra collected according to Chapter 2.4 over the spectral range

7500 - 4000cm-1.

Figure 3.2 – NIR spectra recorded from minerals a.) Quartz, b.) Kaolinite, c.) Anatase,

d.) Albite, e.) Microcline and f.) Goethite

Figure 3.3 – Comparison of NIR spectra collected from soils 4870-2 (low kaolinite 2.2%,

high quartz 89.9%) and 4870-15 (high kaolinite 43.3%, low quartz 24.0%).

Figure 3.4 – NIR Spectra of soils a.) 4870-9 (quartz 96.4%), b.) 4970-15 (kaolinite

43.3%, goethite 11.0%), c.) 4870-19 (anatase 1.1%), d.) 4870-20 (albite 11.9%), and e.)

4870-5 (microcline 9.6%).

Figure 3.5 – Second derivative NIR spectra collected according to Chapter 2.4 over the

spectral range 7500 - 4000cm-1.

Figure 3.6 – NIR spectra recorded from high quartz, low kaolinite soils a.) 4870-2 (89.9%

quartz, 2.2% kaolinite) b.) 4870-4 (77.5% quartz, 4.2% kaolinite) c.) 4870-12 (73.9%

quartz, 6.1% kaolinite).

Figure 3.7 – NIR spectra recorded from kaolinite a.) API #9, b.) KGa-1a, and c.) KGa-2.

Figure 3.8 – Quartz-Kaolinite Mixture: KGa-1a kaolinite mixed with Soil 4870-2 a.) 100 %

kaolinite, b.) 75% kaolinte 25% soil, c.) 55% kaolinte 45% soil, d.) 25% kaolinte 75% soil,

e.) 100 % soil.

Figure 3.9 – Raw spectra recorded from soil 4870-6 at temperatures; a.) Room

temperature, 28oC, b.) 100oC, c.) 200oC, d.) 300oC, e.) 400oC, f.) 500oC, g.) 600oC.

Figure 4.1 – PCA Scores plot of all soils treated according to Chapter 2.1 (excluding

outlier 4870-25 duplicate B). Objects coloured according to the horizon from which they

originated.

Figure 4.2 – Fuzzy Cluster Analysis; PCA Scores Plot of soils prepared according to

Chapter 2.1 and analysed according to Chapter 2.4 (excluding 4870-25 Duplicate B)

a). all objects including those displaying fuzzy membership (black), b). fuzzy objects

removed, leaving only those objects constituting three ideal classes.

Figure 4.3 – Scree plot displaying 10 PC’s and 80% of data variance.

Figure 4.4 – PCA scores plot of all spectra collected from high quartz soils 4870-2, 4870-4

and 4870-12 mixed in varying ratios with three different kaolinite powders, KGa-1a, KGa-2

and API9 according to Chapter 2.5.

Figure 4.5 – PCA scores plot of all spectra collected from high quartz soil, 4870-12,

mixed in varying ratios with three different kaolinite powders, KGa-1a, KGa-2 and API 9.

Figure 4.6 – PC Scores plot a). soil 4870-4 mixed with kaolinite KGa-1a, and b). soil

4870-12 mixed with KGa-2.

Figure 4.7 – PC scores plot displaying the original and duplicate results from soil 4870-4

mixed with kaolinite KGa-1a.

Figure 4.8 – Temperature dependent analysis: PC Scores plot a). soil 4870-25, and b).

soil 4870-18, both heated from room temperature up to 600oC.

Figure 5.1 – PCA scores plot of the complete data matrix including 174 objects (87

duplicate spectra) and 226 variables collected according to methodology outlined in

Chapter 2.7.

Figure 5.2 – PCA scores plots revealing separation based on sampling method, a). 4870-

26 Backyard Soil spectra (excluding outliers). b). 4870-27 Melcann® Sand spectra.

Figure 5.3 – PCA scores plots of Dry Brushed sampling method only, revealing

separation based on shoe type a). Spectra collected from 4870-27 Melcann® Sand using

the Dry Brushed method for the three shoe types. b). Spectra collected from 4870-26

Backyard Soil using the Dry Brushed method for the three shoe types.

Figure 5.4 – PCA scores plots of Wet Sampling method only, revealing separation based

on shoe type a). Spectra collected from Melcann® Sand using the Wet Sampled method

for the three shoe types. b). Spectra collected from Backyard Soil using the Wet Sampled

method.

Figure 5.5 – Triboelectric Series: materials ranked in order of their decreasing tendency

to charge positively, and increasing tendency to charge negatively 110.

Figure 5.6 – Sampling sites recorded during Leather Shoe contact with Melcann® Sand

using Wet Sampling method.

Figure 5.7 – PCA scores plot of spectra collected from the contact of the Leather Shoe

with the Melcann® Sand according to the Wet Sampling method.

Figure 7.1 – Sampling sites for the Leather Shoe (Sample number I Table 7.2) contacting

with 4870-27 Melcann® Sand using the Dry Brushed sampling method.

Figure 7.2 – Sampling sites for the Jogger Shoe (Sample number II Table 7.2) contacting


Figure 7.3 – Sampling sites for the Walk Shoe (Sample number III Table 7.2) contacting


Figure 7.4 – Sampling sites for the Leather Shoe (Sample number IV Table 7.2)

contacting with 4870-26 Backyard Soil using the Dry Brushed sampling method.

Figure 7.5 – Sampling sites for the Jogger Shoe (Sample number V Table 7.2)

contacting with 4870-26 Backyard Soil using the Dry Brushed sampling method.

Figure 7.6 – Sampling sites for the Walk Shoe (Sample number VI Table 7.2) contacting

with 4870-26 Backyard Soil using the Dry Brushed sampling method.

Figure 7.7 – Sampling sites for the Leather Shoe (Sample number VII Table 7.3)

contacting with 4870-27 Melcann® Sand using the Wet Sampled Oven Dried sampling

method.

Figure 7.8 – Sampling sites for the Jogger Shoe (Sample number VIII Table 7.3)


method.

Figure 7.9 – PCA scores plot of spectra collected from the contact of the Jogger Shoe


Figure 7.10 – Sampling sites for the Walk Shoe (Sample number IX Table 7.3)


method.

Figure 7.11 – PCA scores plot of spectra collected from the contact of the Walk Shoe


Figure 7.12 – Sampling sites for the Leather Shoe (Sample number X Table 7.3)

contacting with 4870-26 Backyard Soil using the Wet Sampled Oven Dried sampling

method.


with the Backyard Soil according to the Wet Sampling method.

Figure 7.14 – Sampling sites for the Jogger Shoe (Sample number XI Table 7.3)


method.



Figure 7.16 – Sampling sites for the Walk Shoe (Sample number XII Table 7.3)


method.



Figure 7.18 – ICP Experimental Calibration Plot for SiO2.

Figure 7.19 – ICP Experimental Calibration Plot for CaO.

Figure 7.20 – ICP Experimental Calibration Plot for Fe2O3.

Figure 7.21– ICP Experimental Calibration Plot for MnO.

Figure 7.22 – ICP Experimental Calibration Plot for Al2O3.

Figure 7.23 – ICP Experimental Calibration Plot for TiO2.

Figure 7.24 – ICP Experimental Calibration Plot for K2O.

Figure 7.25 – ICP Experimental Calibration Plot for BaO.

Figure 7.26 – ICP Experimental Calibration Plot for MgO.

Figure 7.27 – ICP Experimental Calibration Plot for Na2O.

Figure 7.28 – ICP Experimental Calibration Plot for SrO.

Figure 7.29 – ICP Experimental Calibration Plot for P2O5.

LIST OF TABLES

Table 1.1 – Summary of Australian Soil Classification Criteria 24, 25.

Table 2.1 – Properties of the high quartz soils used for mixing with kaolinite.

Table 2.2 – Properties of the kaolinites used for mixing with high quartz soils.

Table 2.3 – Ratios of each kaolinite mixed with each high quartz soil.

Table 2.4 – Australian Soil Classification information for the soils used in the temperature

dependent NIR analysis.

Table 3.1 – Summary of XRD Results.

Table 3.2 – Summary of ICP Results.

Table 3.3 – NIR Absorption assignments as compared with literature values.

Table 4.1 – Fuzzy Cluster membership values compared to the XRD results.

Table 4.2 – PROMETHEE out-ranking flows and ranking for Soil 4870-4 mixed with

Kaolinite KGa-1a.

Table 7.1 – Soil Classifications according to Al-Shiekh Khalil et al 69.

Table 7.2 - Sample information and file names for Dry Brushed sampling method.

Table 7.3 - Sample information and file names for Wet sampled, Oven Dried method.

Table 7.4 – ICP Experimental Calibration Data for SiO2.

Table 7.5 – ICP Experimental Calibration Data for CaO.

Table 7.6 – ICP Experimental Calibration Data for Fe2O3.

Table 7.7 – ICP Experimental Calibration Data for MnO.

Table 7.8 – ICP Experimental Calibration Data for Al2O3.

Table 7.9 – ICP Experimental Calibration Data for TiO2.

Table 7.10 – ICP Experimental Calibration Data for K2O.

Table 7.11 – ICP Experimental Calibration Data for BaO.

Table 7.12 – ICP Experimental Calibration Data for MgO.

Table 7.13 – ICP Experimental Calibration Data for Na2O.

Table 7.14 – ICP Experimental Calibration Data for SrO.

Table 7.15 – ICP Experimental Calibration Data for P2O5.

Table 7.16 – ICP experimental Certified Reference Material 2704 (Buffalo River

Sediment) data.

Table 7.17 – Comparison of experimental Certified Reference Material 2704 (Buffalo

River Sediment) measured results with certified values.

ABBREVIATIONS

CCD Charge Couple Device

CID Charge Injection Device

CRM Certified Reference Material

CTD Charge Transfer Device

DRIFT Diffuse Reflectance Infrared Fourier Transform

DTA Differential Thermal Analysis

FC Fuzzy Clustering

FT-IR Fourier Transform Infrared

GAIA Graphical Analysis for Interactive Assistance

ICP Inductively Coupled Plasma

ICP-OES Inductively Coupled Plasma – Optical Emission Spectroscopy

IR Infrared

MCDM Multi-Criteria Decision Making

MSC Multiplicative Scatter Correction

ND Not Detected

NIR Near Infrared

NIST National Institute of Standards and Technology

PCA Principal Component Analysis

PMT Photo Multiplier Tube

PROMETHEE Preference Ranking Organization Method for Enrichment

Evaluations

RACI Royal Australian Chemical Institute

RSD Relative Standard Deviation

SECV Standard Error of Cross Validation

SEM Scanning Electron Microscope

SEV Standard Error of Validation

SNV Standard Normal Variate

SRM Standard Reference Material

TA Thermal Analysis

XRD X-Ray Diffraction

1.0 INTRODUCTION

1.1 Prologue to the Investigation

Advancements in Chemometrics has facilitated the use of NIR spectroscopy in the

food, agriculture, pharmaceutical, chemical, and polymer industries 1-5. The analysis of

soil for forensic purposes has perhaps not received the detailed attention that it might

deserve 6. There is no doubt that soils have proved difficult and often fruitless in many

crime investigations, but much potentially useful information is locked up in even small

quantities of soil 7.

The characterisation of soils can be of vital importance for linking an offender or

object to the scene of the crime and/or victim/s involved. Soil may also be relevant to a

case because of its nature and distribution and how these relate to the circumstances of

the crime. Soils tend often to be found as smears on clothing, deposits on shoes or mud

adhering to tools, automobiles and other implements associated with crime. As with most

types of physical evidence, soil is comparative in nature; when it is found in the

possession of a suspect it must be carefully collected and compared with soil sampled

from the crime scene and surrounding vicinity 8.

Forensic geology is integrative in nature combining standard methods and

information from a wide range of scientific disciplines such as physics, chemistry and

biology 9. Natural soil properties show large spatial variation as a result of the interaction

of five main soil forming factors: 1.) climate, 2.) topography, 3.) biota, 4.) time, and 5.)

parent material.

A variety of comparative techniques are employed to determine the bulk

properties of soil. A forensic comparison often begins with simple, non-destructive

techniques such as assessment of colour, texture, density distribution and particle size.

Further, more involved methods may include polarised microscopy, emission/absorption

spectroscopy, thermal analysis or X-ray diffraction 10.

Many bulk properties can be determined relatively quickly with simple equipment

at a low cost, but others require the use of highly sophisticated equipment, longer time

periods and significantly higher costs. Bulk properties vary in terms of their discriminatory

power, and an understanding of this is critical for correct interpretation of the significance

of results 11. In most circumstances, it is desirable to make use of several different

techniques that provide information about a range of physical, chemical, mineralogical,

and biological characteristics.

Methods currently available are capable of exploring links between soils which

were collected in relation to a crime. However, it would be advantageous if a method was

available that was able to analyse soil ‘in-situ’ at a crime scene. This would be especially

useful if the method could be applied to an impression, for example a shoe sole

impression or tyre tracks, which remain at a crime scene. Therefore, a link could be

established directly between the impression and the item which may have caused the

impression. If such a method were possible, it would also be beneficial in strengthening

the chain of evidence.

This research introduces a potential application of Infrared spectroscopy to collect

comparative information about soil in a rather different manner to that currently available.

The project aims to investigate the ability of Near-Infrared spectroscopy and

Chemometrics to compare various soils. The method has great potential in the forensic

discipline as it is a form of rapid analysis that requires little to no sample preparation and

limited operator training with the potential for ‘at field’ analysis. The technique also has

the ability to be totally portable, allowing the device to be easily transported to the crime

scene as part of the mobile crime scene unit. Another advantage of the proposed

method is that it requires only a small amount of sample for analysis. Furthermore, the

method is non-destructive permitting additional analysis to be completed at a later date

by other methods if required. To date, no study has applied NIR spectroscopy to soils

with this intended application.

The overall aim of this project is to research the possibilities of bringing together

NIR spectroscopy and Chemometrics with a common purpose to ascertain whether this

approach can either discriminate or match soil samples for use in a forensic investigation.

Within the scope of the research, the following questions arise.

- Is Near-Infrared spectroscopy combined with Chemometrics capable of

distinguishing between soils?

- If so, on what foundations is this distinction based?

- Can this distinction between soils be applied to a forensic scenario?

- What are the limits and capabilities of this application?

These questions were investigated on the basis of the following primary objectives:

• Obtain a series of surface and core soil samples and characterise them using

XRD and ICP methods.

• Analyse the same samples using NIR spectroscopy, collecting duplicate spectra

from each soil.

• Locate and identify the key NIR absorption regions which relate to the

composition of these soils.

• Use Chemometrics methods to explore whether NIR is capable of distinguishing

between soils and then outline how and why the distinction is apparent.

• Simulate a forensic scenario as an exploratory investigation to establish the

capabilities of such a method applied in the ‘real world‘.

The research is presented in the following chapters,

Chapter 1 – provides a review of soil evidence throughout history, the formation of soils

over time as well as soil pre-treatment, analysis and sampling methods. Background

theory behind the methods and instrumentation used during this investigation are also

detailed in this chapter.

Chapter 2 – describes the materials, methods, apparatus and experimental procedures

employed in this study.

Chapter 3 – is concerned with the characterisation of the soils by X-ray Diffraction and

Inductively Coupled Plasma Optical Emission Spectroscopy methods. It also discusses

the recorded NIR spectra in three sections: Raw soils, Quartz Kaolinite mixtures, and,

Temperature dependent spectra. Discussion revolves around the absorption frequencies

and how such assignments are related to the XRD and ICP results.

Chapter 4 – considers the application of Chemometrics and Multivariate Data Analysis of

the recorded NIR spectra. Correlations and trends that are observable from variations in

the NIR method will be discussed in depth.

Chapter 5 – the results from the previous chapters are combined and applied in order to

simulate a possible application to a forensic scenario. Essentially, this chapter relates to

the overall aim of ‘combining NIR and Chemometrics to discriminate or match soils in a

forensic investigation’.

Chapter 6 – summarises the major findings and conclusions which arose from this

research. Suggestions for further work to expand and strengthen this work are also

included.

1.2 The Development of Soil Evidence in Forensic Science

The application of geology to forensic science began more than 100 years ago in

the fictional series titled ‘Sherlock Holmes’ by Sir Arthur Conan Doyle 8. At approximately

the same time, Hans Gross of Austria published a book, which stated that dirt on shoes

can often tell us more about where the wearer of the shoes had been rather than toilsome

inquiries 12. A forensic science pioneer, Frenchman, Edmond Locard later demonstrated

that when a person or object comes in contact with another person or object, a cross

transfer of evidence occurs 13. This theory is known as the Locards’ Exchange Principle

and governs the transfer of all trace evidence crucial in forensic analysis. According to

Murray and Tedrow 14, 1904 saw the first examination of soil evidence, by Georg Popp of

Germany, which led to the conviction of an accused. Since the early 1900’s, forensic

analysis of soil has improved and developed remarkably into a precise science highly

regarded in the modern court of law 15.

Before the evidentiary value and methods of analysis relating to soil can be

discussed, it is necessary to define what is meant by the term ‘soil’ in forensic science.

The term ‘soil’ includes the disintegrated surface material, artificial or natural, that lies on

or near the earth's surface 8. The forensic examination of soil is not only concerned with

the analysis of rocks and minerals but also of artificial or foreign objects such a glass

fragments, paint chips, pollen and small insects that may impart a degree of uniqueness

to a soil sample collected at a crime scene.

Soil can provide useful information linking people to a crime scene because of its

nature as the surface of the ground 12. The evidentiary value of soil depends largely upon

variation in its characteristics. Soil is very complex not only in its components such as

minerals, oxides, organic matter, micro-organisms and materials but also in its physical

nature such as particle size and porosity. The diversity of soil arises from the many soil

forming processes namely, topography, biota, time and climate, and how these affect the

various types of parent materials. The value of soil evidence rests with its prevalence at

the crime scene and its transferability between the scene and the suspect. Forensic

analysis of soil is comparative in nature 16. Hence, complexity and diversity in

composition of soil permits high discriminatory power between samples. Properties of soil

that can be observed or measured directly, and for which large variation exists, offer the

greatest evidentiary value 17.

1.3 The Five Soil Forming Factors

The formation of soils is determined by several processes and a number of

environmental factors operating over time to produce soil at any one location. The

processes and environmental conditions vital to the formation of soils are, climate,

topography, biota, time and parent material 18. The following is a brief discussion of each

of these five factors and the important role they play in the formation of soils.

1.3.1 CLIMATE

On a global scale, there is a strong correlation between soil properties and

climatic zones. However, at regional and local levels this correlation is not as prevalent

and becomes more complex 18. Three important climatic variables that influence the

formation of soils are temperature, moisture and wind. Temperature has a direct

influence on the weathering of bedrock, the activity of soil micro-organisms, the frequency

and magnitude of physical and chemical reactions within the soil, and the rate of plant

growth. High moisture availability in soil also plays a vital role in promoting the weathering

of bedrock, sediment development, chemical reactions and plant growth. The availability

of moisture has an influence on soil pH and the decomposition of organic matter 18. The

level of rainfall in a region governs evaporation, plant growth, soil run-off, moisture

content and erosion. The final climatic factor, wind, plays a vital role, especially in arid

regions, in the distribution of coarse particles such as sand.

1.3.2 TOPOGRAPHY

Topography refers to the configuration and elevation of the land surface. It

generally modifies the development of soil on a local or regional scale. Soil properties

found to be related to topography are depth of soil, thickness of leaf matter coverage,

relative wetness, soluble salt content, and horizon differentiation 19. Drainage enhances

illuviation and eluviation, which are largely responsible for the development of soil

horizons (Chapter 1.4). Illuviation is the deposition of humus, chemical substances, and

fine clay mineral particles to the lower layers of a soil from upper layers because of the

downward movement of water through the soil profile 20. Soils which develop on moderate

to gentle slopes are often better drained than soils found at the bottom of valleys. Steep

topographic gradients inhibit the development of soils as a result of erosion, and the soil

being constantly washed away.

1.3.3 BIOTA

Biota refers to all organisms including plants and animals from large mammals to

micro-organisms. Higher plants contribute to soil formation through the addition of litter,

such as leaf matter, to the soil surface. Micro-organisms, including bacteria, fungi,

protozoa and algae, play a vital role which is poorly understood by many scientists 20.

Earthworms, termites, ants, nematodes and millipedes are responsible for much of the

mixing of soils through ingestion, digestion and expulsion of organic material. Man and

large animals also have a vast influence on the disturbance of soils. Mixing of soil is

collectively termed pedoturbation and can obliterate horizon development 20.

1.3.4 TIME

Soil formation is a slow process requiring thousands or even millions of years 21.

The arrangement of horizons within soil is known as a soil profile (Chapter 1.4). Soil

profiles develop over time and their properties depend on how long soil forming

processes operate in relation to the other factors. Fitzpatrick suggests that weathering of

rocks to form soils such as red earths can take more than one million years 18.

1.3.5 PARENT MATERIAL

It can be difficult to asses the role of parent material in soil formation. Parent

material often provides only the framework within which other factors and processes

operate 20. It is well known that soils develop from consolidated rock and other materials

through weathering and disintegration. The dominant parent material of all mineral soils

is weathered rock, either formed in situ or elsewhere, and somehow transported to the

site. The type of rock from which the soil is derived determines many characteristics

such as chemical composition of the developing soil 18.

When one considers that soil material can be developed on any one of the almost

unlimited types of parent material and modified by a large number of different climates,

one can begin to appreciate the potential diversity of soil characteristics. Combining this

with the effects of transportation of particles, pedoturbation, dissolved material, plant

growth and nutrient uptake, one begins to appreciate the extraordinary variation in soils.

Geologists are concerned with the variation between soils and employ a variety of

standard methods to measure or define these variations. Forensic scientists are more

interested in comparing soils, employing specific techniques combining geology,

chemistry and soil science to distinguish between samples.

Figure 1.1 – Schematic diagram illustrating the horizontal distinctions in a soil profile.

O – Layer dominated by organic material in various stages of decomposition

Illuvial Zone

Eluvial Zone

A – Mineral horizon with accumulation of humified, decomposed organic matter

B – Mineral horizon dominated by illuvial accumulations of clay, iron, aluminium, humus etc.

C - Mineral horizon, unconsolidated, may retain some rock structure

E – Light or bleached, zone of maximum leaching, low levels of clay, iron, aluminium, humus etc.

R – Unaltered consolidate bedrock

Soil Surface

1.4 Soil Profiles & Horizons

Soil is not a random assemblage of organic and inorganic constituents, rather an

ordered vertical structure consisting of many horizontal distinctions. A soil profile (Figure

1.1) is a vertical section of soil from the surface through all horizons to the parent or

substrate material 22 usually described within 1.5–2.0m unless bedrock is shallower. Soil

horizons are the vertical sections within the soil profile assigned by an expert based on

properties such as colour, texture, particle size, the presence of mottles and course

fragments 18. The change from one horizon to another varies in degree of sharpness and

outline. Colour is usually the most obvious change from one horizon to another, but

structural and textural changes may also be evident. In some soils, horizons are clear

and relatively unambiguous, but in many soils it becomes difficult to define horizons and

the taxonomy becomes somewhat subjective.

A soil profile commences with unaltered bedrock, termed the R layer, which is

technically not a soil horizon but consolidated rock 20. As weathering progresses, the R

layer is transformed into a mineral horizon of unconsolidated material known as the C

horizon. The C horizon is scarcely affected by biological activity. The B horizon lies

above the C Horizon and typically has higher clay content with fragmented bedrock.

At the ground surface, addition of organic material produces a layer differing from

the rest of the soil by the amount of organic matter present. O horizons, are dominated

by organic materials and decomposing debris. O horizons are not always present

depending upon the surrounding vegetation, time of year, slope and traffic in the area.

Beneath the O Horizon lies the A horizon, a dark mineral layer commonly termed ‘topsoil’.

Fragments of organic matter, dead plant and animal fragments, seeds and pollen grains

can be found amongst the bulk mineral content 23. The O and A horizons are most

frequently encountered in forensic soil analysis due to surface contact and frequent

human interaction.

In addition to the main horizons described above, several descriptors exist to add

further explanation of sub-divisions within a horizon. Each major horizon may be divided

into sub-horizons by the addition of a numerical subscript, based on minor shifts in colour

or texture with increasing depth. For example, a B horizon may exhibit a slight change in

texture or colour and thus to distinguish this B1 and B2 horizons will be described.

Suffixes also exist describing further physical features often visible within a horizon.

Since such suffixes are not used in this thesis no further discussion is warranted.

Table 1.1 Summary of Australian Soil Classification Criteria 24, 25

Soil Order Suborder Properties

Anthroposols Cumulic, Hortic, Garbic, Urbic, Scalpic, Dredgic and Spolic

Resulting from human activity

Calcarosols Hypocalcic, Supracalcic, Hypercalcic, Lithocalcic, Hypergypsic, Shelly and Calcic

High calcium carbonate content, shallow depth, low water retention, high salinity, sodicity and alkalinity

Chromosols Red, Brown, Yellow, Black and Grey

Clay content increases down the soil profile

Dermosols Red, Brown, Yellow, Black and Grey

Strongly acidic in high rainfall areas or highly alkaline containing calcium carbonate

Ferrosols Red, Brown, Yellow, Black and Grey

A high free iron and clay content which can lack strong textural contrast between horizons A and B

Hydrosols Supratidal, Extratidal, Hypersalic, Salic, Redoxic and Oxyaquic

Seasonally or permanently wet soils, high potential drainage of acid sulphate.

Kandosols Red, Brown, Yellow, Black and Grey

Well drained, permeable soils

Kurosols Red, Brown, Yellow, Black and Grey

Strongly acidic soils with an abrupt increase in clay down the profile.

Organosols Fibric, Hemic, Sparic Dominated by organic matter

Podosols Aeric, Semiaquic and Aquic

Largely controlled by organic matter and aluminium, with or without iron

Rudosols Hypergypsic, Hypersalic, Shelly, Carbic, Arenic, Stratic, Leptic and Clastic

Minimal soil development due to propeties and occurrence in arrid regions

Sodosols Red, Brown, Yellow, Black and Grey

High sodium content which may lead to soil dispersion and instability.

Tenosols Chernic, Bleached-Orthic, Orthic, Chernic-Leptic, Leptic and Bleached Leptic

Poor water retention, almost universal low fertility

Vertosols Aquic, Red, Brown, Yellow, Grey, Black Vertosol

High smectite content causing soil to shrink and crack when dry and swell when wet.

1.5 Soil Classification

Soils are three dimensional bodies and their classification has always caused

problems for scientists and those who use the land to create a living. Problems occur

due to the almost unlimited types of parent material that may be modified by a large

number of different climates. Combining this with other effects such as pedoturbation,

transportation of particles, the presence of diverse colloidal particles, dissolved material

and nutrient uptake, it is not surprising that an unlimited number of variations in soils

exist.

Soil classification is not consistent all over the world, with each country developing

a special classification according to the soils apparent in that country. The criteria for the

classification of soils in Australia was developed by Isbell 22 and Jaquier et al. 25.

Australia has a large diversity of soils, many of which are infertile, ancient and strongly

weathered. This classification process includes 14 orders, all of which are summarised in

Table 1.1.

1.6 Soil Sampling in Forensic Science

Soils frequently disturbed or altered by recurrent human contact, machinery or

cultivation would normally be deemed unsuitable for academic study because of the high

alteration and contamination of the site. The forensic scientist must spend much time

sampling in such areas because they are scenes of greatest human activity and therefore

large variation. Forensic soil samples are seldom collected from undisturbed locations.

Instead they come from areas around the home, parking lots, highway shoulders or rural

properties. The forensic scientist is therefore often unable to be selective of sampling

sites as these are largely determined by the events of the crime or actions of the

perpetrator.

1.6.1 FACTORS GOVERNING THE COLLECTION OF FORENSIC SOIL SAMPLES

In most forensic applications of soil studies, two separate types of samples

emerge. The first, over which a forensic scientist has limited control, arises because the

sample or samples are directly associated with the crime or incident. These samples

take a variety of forms, such as a mass of soil on a highway at the scene of an accident,

soil traces in/on shoes or clothing, rocks used as weapons and dust found in hair. For

the other type of forensic soil sample, the forensic scientist does have some extent of

control over. These are samples taken for comparison with samples associated with the

crime or incident. Such samples may include soil removed from the frame and fenders of

a suspects’ vehicle, soil in tyre treads and mud guards and soils taken from a particular

crime scene or known location such as a suspect’s garden.

Samples determined by the events of the crime are often a result of a mistake of

the perpetrator. In this situation, usually no attempt is made by the persons involved in

the crime to collect a sample that is representative of the bulk. Therefore, it cannot be

expected that a control sample will be the same as an accidentally collected sample. The

material contained in the sample accidentally collected may, in part, represent the grains

that are loose or easily broken. In such cases, professional judgement is required to

choose methods of analysis which will be capable of establishing a comparison or lack

there of.

1.6.2 METHODS FOR COLLECTING FORENSIC SOIL SAMPLES

Sampling solid objects is among the more complicated operations owing to the

typical heterogeneity in composition and properties particularly relating to the degree of

compaction. Two main soil sampling methods are employed for laboratory studies;

Disturbed and Undisturbed. Disturbed samples are typically collected if chemical or

physical analyses such as particle size determination are to be performed. Undisturbed

samples, often obtained through coring, are collected if physical examinations such as

determination of horizons are required. A final sampling technique, termed Box sampling

and belonging to the Undisturbed sampling category, is employed when the analysis to

be performed is of the micro-morphological variety.

The first and most fundamental method of soil sampling and storage is Disturbed

sampling using a bag. Disturbed samples are collected by loosening the soil in a profile

with a spade or trowel. The sample is transferred into a plastic bag, hence the term ‘bag

sampling’. Conventionally, most laboratory determinations on soils are made with fine

earth that passes through a 2mm sieve 26. Although many standard soil analyses require

only a few grams of soil, samples weighing 1-2 kg are often collected from relatively

stoneless soils 27. If sufficient sample sizes are not available, the sampler must keep in

mind that even a few grains of sample is better than no sample at all.

A reliable and standard sampling method which falls into the Undisturbed

sampling category, is a Kubiena Frame. A Kubiena frame is a small rectangular metal

frame with protective lids, top and bottom 26. To use the frame, the protective lid is

removed and the frame pressed gently into the soil. A sharp knife may be required to cut

an indent for the frame to slip into easily with minimal compression or disturbance to the

soil. The sample is sealed with the lid where the soil remains protected and undisturbed

until analysis is performed. By opening a corner of the box, the frame can be opened out

so that the sample can be removed easily with minimal damage. Variations of the

Kubiena frame were developed by several pedologists including Brewer, Vanderford and

Kasatkin 26.

Hand sampling tools are commonly used to collect samples requiring

determinations of physical properties. Hand sampling tools similar to those described by

Dagg and Hosegood 28 are employed to obtain undisturbed cylindrical, vertical cores. A

sampling sleeve is inserted into the head of the tool and a cutting ring placed in position.

The sampling tool is placed vertically on a soil surface and is driven into the soil with

steady blows from the heavy cylindrical hammer that slides up and down the main shaft

of the tool. Once the tool has penetrated the soil up to a sufficient depth, the tool along

with the sample is carefully removed. The cutting ring is removed, the sleeve trimmed

with a sharp knife and a lid slipped on to enclose the sample. This method is typically

used for determinations of soil physical properties such as bulk density, distribution of

pores, soil permeability and soil moisture release characteristics 28.

Larger scale coring is another commonly employed method of undisturbed soil

sampling. Various core sampling devices exist, both hand and power driven 29. The latter

can be mounted onto vehicles of varying sizes for ease of movement through the field.

Backhoes and trenches may also be used for some applications. These machines tend

to introduce varying degrees of contamination, pedoturbation and disruption to the

sample, and hence, may only be used for limited applications.

Soil sampling by a forensic scientist may require the removal of material from a

suspects’ shoe, clothing, vehicle, shipping container etc. This can be done by a variety of

methods. If a lump of soil is involved it should be collected and preserved intact.

Preservation of the layers of soil, for example from underneath a suspect’s car, is

especially important. Layers permit the study of the stratigraphy or particles of different

layers from youngest to oldest 14. This layering effect may serve to impart soil with

greater variation, and hence greater evidentiary value, than that which is normally

3

5

24

7

~1m

~1m

8

~1m

6

~1m1

Site of the Impression

<10m

<10m

<10m

3

5

24

7

~1m

~1m

8

~1m

6

~1m1

Site of the Impression

<10m

<10m

<10m

Figure 1.2 – Interpretation of Cunningham’s proposed method of sampling

soil from tyre and shoe impressions adapted from 30.

NB: Numbers denote suggested sampling sequence. Not to scale.

associated with looser soil. Soil particles from a person’s clothing are often collected by

shaking the garment over a clean sheet of paper. In some cases a vacuum cleaner has

been used. However, this method is unsatisfactory as lumps are shattered, the physical

appearances of particles are altered and contamination from the vacuum can occur.

Adhesive tape can be useful in removing fine particles but this method is deemed

unsatisfactory for soil samples as the adhesive tape interferes with many soil analyses

and it is difficult to remove particles which remain unaffected by the tape 14.

Specific methods should be followed when collecting soil from difficult locations.

In the case of sampling soil material adhered to the underside of a vehicle, separate

samples, preserved as intact layers should be removed from under all four fenders. Oil

or grease with contaminated minerals, rocks and related materials should be sampled

from several places under a vehicle when these are to be studied in conjunction with

similar residues left at an accident scene. Where a crime scene involves a vertical cut

into the earth, such as a quarry or gravesite, samples should be taken from each layer or

horizon that exhibits a difference to the eye in colour, texture, or mineralogy.

1.6.3 SOIL SAMPLING SITES

In the collection of exemplar soil, numerous samples should be collected at

varying distances from the suspected point of origin. The actual number of soil samples

to be collected depends upon the heterogeneity of the soil in the area. According to a

typical soil examination protocol proposed by Thornton 16, at least five samples and

perhaps as many as twenty samples are ordinarily collected. The distance from the

suspected point of origin at which samples are taken will again be dependent upon the

heterogeneity of the soil. According to Thornton 16, several samples will normally be

collected within three metres of the suspected point of origin, and several more at

distances of up to thirty metres.

A sampling method of collecting soil samples from foot and tyre impressions and

surrounding areas has been proposed by Cunningham 30. An initial sample is collected

from the actual impression, after casts and photographs have been completed.

Additional samples are systematically collected from the area around the impression.

The sites chosen for sampling around the impression should be measured from the point

of the first sample and the measurements recorded. Samples are collected at the four

points of the compass, about one metre from the initial sampling site. Several additional

samples should then be obtained at a distance of up to ten metres from the initial

sampling site. This proposed method is illustrated in Figure 1.2.

1.7 Soil Pre-treatments

Procedures for pre-treatment of soils are often deceptively simple 31. Methods

employed for the pre-treatment of soils include washing, drying, crushing, grinding,

coning and quartering and extracting. It is important to consider when pre-treating a

sample that contamination or analyte loss do not occur. Many soils are biologically active

and washing or prolonged heating may alter the composition of the sample. The thermal

stability, volatility and degradation of a sample should be considered before pre-

treatments are performed.

Even a simple procedure such as washing may extract an analyte. It is

consequently often preferable to avoid washing altogether if a suitably clean sample is

obtained. An alternative to washing is soft brushing to remove debris. The cleanliness of

the sample is particularly important for trace metal determinations where the

concentration may be higher in surrounding soil.

The temperature and duration of drying must be a balance between too low a

temperature over a lengthy period promoting biological activity and too high a

temperature over a shorter time period leading to loss of volatile components. A typical

drying procedure would be to expose as much surface soil to circulating air and by

elevating the temperature. This is usually done in a sealed oven where the temperature

reaches approximately 105oC32 as significant changes in the physio-chemical properties

of the soil can occur at elevated temperatures. The main properties of soil which may be

subject to change upon drying are,

- Salts present in the sample will become more concentrated and may crystallize on

the surface.

- Some minerals may oxidise or be subject to other alterations resulting in a colour

change. This is especially true of samples containing high levels of iron or black

sulphur bearing muds from swamps or marshes.

- Nitrate content tends to increase with drying.

- Microbial population and activity is largely altered, and

- Soil colour tends to become lighter upon drying.

Following drying, a soil sample is usually crushed, either by hand using a mortar

and pestle or using a mechanical device. Most tests require soils to be ground or

crushed to pass through a 2mm sieve 33. The aim of grinding is to achieve a sufficient

degree of homogenisation. Other equipment that exists for grinding soils are roller mills,

hammer mills or brush mills. The grinding procedure has the potential for contamination

to occur, either from the composition of contacting surfaces or from deposition of previous

sample residue.

Coning and quartering is a method applied in order to achieve unbiased sub-

sampling of soils. In this method, a pile of soil is placed onto a large flexible sheet of

either plastic or paper. Alternative corners of the sheet are lifted sequentially to allow

sufficient mixing of the sample. The mixed pile is divided into quarters where opposite

quarters are combined and the remaining are discarded. The process is repeated as

many times as necessary to obtain the quantity desired for analysis. Each time the

process is repeated the sample size is approximately halved. Accuracy in obtaining a

representative sub-sample is crucial to the outcome of the analysis.

Some laboratory methods require an extraction to be performed prior to analysis.

An extraction is usually only performed if heavy metals or organic contaminants are to be

determined. If present, metal and organic contaminants are likely in very low

concentrations. The simplest method of extraction of organics is to shake a sub-sample

immersed in an organic solvent. Extraction reagents were developed for specific

applications in the mid 1900’s and are now considered to be standard procedures 34. The

shape and size of the extraction vessel, shaking speed and temperature can have a

significant effect on the extraction of specific elements. Control of these features is

essential if the obtained assay result is to be reliable.

1.8 Current Methods of Forensic Soil Analysis

During the last quarter of a century, there have been revolutionary changes in

methods used in soil analysis involving modern sophisticated instruments. These

modern instruments are used in data gathering for the identification of elements,

compounds and minerals. Techniques initially used in the forensic comparison of soils

were lengthy and tedious and are collectively referred to as Wet Chemical methods 35.

Several articles have been written describing the importance of soil evidence and the

contribution of geology to criminal investigations 36-39.

Before the invention of modern instruments, soil and geological samples were

analysed using wet chemical methods 17. Chemical analyses of soils, sediments and

rocks are carried out by a number of procedures. These methods include moisture

content, loss on ignition, organic matter, ion exchange capacity, pH and conductivity.

Although there is no one unique method without limitations, there are a number of

available methods that are considered satisfactory 40. Such methods are basic and

were not utilised in this study, and hence, further discussion is not warranted.

1.8.1 COMMON FORENSIC METHODS

The forensic comparison of soils often begins with a visual inspection using a low

power stereozoom microscope. Objects as small as approximately 10 microns in

diameter may be viewed using this technique 6. A stereozoom microscope enables the

visualisation and possible identification of extraneous matter or unique particles such as

paint chips, weld spatter, glass fragments or pollen spores. If extraneous matter is

identified, such material is removed to allow for further examination by an appropriate

expert. The evidentiary value of extraneous material lies in how common and widely

distributed the matter is. It is also common at this stage of analysis to observe briefly the

types of grains and particles present.

Colour is one of the most important identifying characteristics of all minerals and

soils 41. Colours are present as a result of organic matter and mineral composition and

range from greys, yellows, browns, reds, blacks and even greens or purples 17. In order to

establish uniformity in colour descriptions, standards are employed such as the Munsell

Colour Chart 14. The Munsell colour standards are established on three factors; the hue,

value and chroma. A typical colour defined by the Munsell colour chart is 10YR8/3

where the hue (10YR) refers to the dominant spectral colour, the value (8) to the degree

of lightness or darkness and the chroma (3) to the purity of the spectral colour. The

standardisation of colours offers a certain degree of uniformity, but the moisture content

also affects the colour of a soil as does the light under which the soil is viewed. It is

therefore not uncommon that a sample be dried prior to assessing the colour or that an

estimated degree of ‘wetness’ is recorded with the colour. Characteristic red or greenish-

yellow soils may be indicative of specific regions. However this test does not hold high

diagnostic value as to the origin of a sample as many vastly different soils from various

origins may have similar colours.

Particle Size Distribution is a fundamental property of soil which is commonly

considered when comparing soils for forensic purposes. Two methods used for the

separation of particles by size are the passing of the sample through a nest of sieves or

determining the settlement rate of particles within a fluid 14. As individual soil grains tend

to aggregate the need to separate soil arises. This is achieved by various pre-treatments

including but not limited to hydrochloric acid and hydrogen peroxide 14. Obviously, all

samples to be compared must be treated in the same manner. It must also be determined

prior to the treatment that important information will not be lost or altered during the

process.

Quantitative sieving of soils is a method which has been performed for

approximately twenty-five years 17. The method requires a weighed quantity of soil

sample to be sieved through a nest of sieves and each fraction weighed. It is critical in

this method to ensure the time spent sieving for each of the samples is equal so as not to

introduce bias to the results. In some cases it is desirable to sieve the soil dispersed in a

liquid, usually water 14, in order to achieve optimal separation of aggregated particles. A

major advantage of the sieving method is the entire sample material is recoverable. A

disadvantage of this technique is that it requires a relatively large quantity of soil.

Methods alternative to sieving are based on Stokes Law 42. The hydrometer

method relies on the principle of decreasing density within a suspension as the solid

particles settle out 43. This method is capable of determining the percentage of sand, silt

and clay in a sample. This method, though rapid and accurate, is unsatisfactory if

subsequent analysis is required. Results for all particle size distribution methods require

a plot of grain size versus percentage weight in the format of a histogram or continuous

curve. The problem then arises in interpretation of these plots, determining if two curves,

and thus two samples, are similar or not. Size distribution data may be subjected to

quantitative and statistical methods for determining similarity if required.

Density gradient distribution is another useful measurement associated with

forensic soil analysis. A set of several glass tubes sealed at the bottom are filled with

varying ratios of liquids with differing densities. The liquids vary between laboratories

with bromobenzene and bromoform being two liquids which are commonly used. A

simple 8-10 step density gradient is usually sufficient to obtain a usable resolution for soil

samples 17. Equal values of samples are sieved and accurately weighed before being

carefully added to the gradient tubes. Within a few hours the individual particles settle to

a level in the column where the liquid has the same density as the individual particle. To

make the separation patterns comparable the columns must be prepared in a strictly

identical manner. The value of the method lies in the ease of which comparisons can be

made.

Mineral grains constitute the bulk of soil samples, providing the basis for colour,

texture, particle size and density distribution methods. Soil minerals can be classified into

two groups, primary and secondary 15. Primary minerals are formed at elevated

temperatures and are inherited from the parent rock. They make up the main part of the

sand and silt fractions for most soils. The most abundant primary minerals are silica and

feldspar. Secondary minerals are formed by low temperature reactions and weathering

of parent material. Examples of typical secondary minerals are kaolinite, montmorillonite

and illite.

Countless minerals exist and it is not in the scope of this research to identify or

define such a variety. It is, however, in the scope of this chapter to outline the methods

used in forensic analysis to identify such minerals. Instrumental techniques previously

applied to the forensic identification of minerals include polarised light microscopy,

scanning electron microscopy, x-ray diffraction and thermal analysis. A simple outline of

each instrumental method will summarise its application and value in forensic soil

analysis. Further references detailing the theory that lead to the development of these

instruments and their initial application to forensic soil analysis may be found in the

following references 41, 44-46.

1.8.2 INSTRUMENTAL METHODS

The standard technique performed by forensic scientists in order to identify the

minerals present within a soil sample is polarising light microscopy. Sample preparation

requires the soil to be sieved to retain particles with a diameter between 90 and 180µm 14. The surface coating of fine silt clay or humus must also be removed. This is achieved

with the aid of a surfactant and sonication followed by centrifugation. The supernatant

liquid is decanted off and the process is repeated until the liquid becomes clear. The

remaining mineral fraction is dried and the sample is ready for microscopic analysis.

The interaction of the polarised light with a mineral can be used as a diagnostic

tool in identifying the mineral. Isotropic minerals allow light of all angles to pass through

with a constant velocity and path 47. Fortunately few minerals are isotropic and most

minerals are anisotropic. Anisotropic minerals display varied properties when polarised

light travels through the grain at different directions 47 causing the light to be split into

multiple rays. The rays do not necessarily travel at the same velocity or follow the same

pathway. The term, birefringence, describes the difference in velocity of these rays 48.

When the rays emerge from the mineral grain, they combine to produce a range of

interference colours. A Michel-Levy chart summarises the relationship between

interference colours, birefringence and grain thickness, making it possible to identify the

specific minerals present within the soil sample. Isotropic and anisotropic minerals are

easily distinguished because isotropic minerals do not transmit polarised light when

viewed with cross polarisers, and hence, appear black when viewed under plane

polarised light.

The Scanning Electron Microscope (SEM) proves very useful in forensic work

because it is possible to examine particles at very high magnifications, thus bringing out

details that would otherwise go undetected 49. The depth of field is large and most SEM

images have an excellent three-dimensional appearance. Surface features of individual

mineral grains are visualised displaying surface effects such as scratching and

smoothing. These surface features are often representative of the history of the

individual grain 14. It is also common to see individual clay flakes which fill these surface

scratches. This is another characteristic potentially valuable when discriminating between

the minerals present and the process of degradation over time.

X-ray Diffraction (XRD) is one of the most important and reliable methods of

identifying the composition of geological soil and other crystalline structures 40. The

method is based on the arrangement of atoms, ions and molecules within a crystalline

structure. X-ray diffraction is capable of distinguishing between, for example, pure

carbon in graphite form and pure carbon in diamond form as the crystalline structures are

different. The sample is analysed by passing X-rays through the crystal and measuring

the angle of diffracted X-rays. The interpretation of X-ray diffractograms relies upon

Braggs law, specifically the d spacing and the intensity. Each crystalline material has its

own distinctive X-ray pattern which is compared to either a reference database or a

pattern produced by a known mineral for identification 40. If a simple comparison between

samples is required then the X-ray diffractograms may be easily compared without

identification. Refer to Chapter 1.11 for more information on XRD.

The development of forensic soil comparison has incorporated a variety of

techniques ranging from quite basic chemical methods to complex instrumental

techniques. Techniques differ widely in the information produced and the ease of

analysis performed. However, the basis of all methods employed in forensic science is to

establish a degree of comparison between samples. Analysis stops once sufficient

distinction between samples can be established. The diverse complexity between

samples arises due to the environmental soil forming factors, climate, topography, biota,

time and parent material. Soil analysis can also be extended to include extraneous

matter located in a soil sample. The goal of soil comparison is to establish if the material

was or was not derived from a particular location, thereby associating or disassociating a

person or object with a specific location. The comparison of earth materials or changes

in materials may also be used to determine when an incident occurred, the cause of an

incident or the responsibility for an incident.

1.9 Vibrational Spectroscopy

Vibrational spectroscopy encompasses both infrared (IR) and Raman

spectroscopy. The vibrational motion of molecules as probed by IR absorption and

Raman scattering can be analysed to determine the molecular structure of a diverse

range of materials 50. IR spectroscopy is concerned with the absorption of light by a

sample, while Raman spectroscopy is concerned with the light inelastically scattered by

the sample. The vibrational analysis of small molecules, present in the gaseous state,

can reveal the rotational substructure as well as bond lengths and angles. In solid and

liquid phases, this rotational structural information is generally lost. However, molecular

symmetry and the presence of various functional groups can still be obtained from

vibrational spectra 51. Infrared spectroscopy is the methodology employed in this research

and is therefore the focus of this section.

1.9.1 INFRARED (IR) SPECTROSCOPY

Infrared spectroscopy is the study of the interaction of infrared light with matter 52.

The infrared electromagnetic spectrum is essentially divided into three subregions each

having unique applications and instrumental design 53. The primary and most important

region is that of mid-infrared commonly defined as the region between 4000-400cm-1

which reveals information relating to fundamental vibrations. The upper region, above

4000cm-1, known as the near-infrared region, is often utilised in industrial qualitative and

quantitative methods of analysis. The final region, far-infrared, typically below 400cm-1

relates to libration.

Radiation in any section of the electromagnetic spectrum is characterised by its

wavelength, λ, and frequency, ν. The wave nature of radiation may be viewed as an

oscillating electric field, together with an oscillating magnetic field that is perpendicular to

the former, with both fields orthogonal to the direction of travel of the wave 51 (Figure 1.3).

It is often said that radiation behaves as if it were comprised of particles. These particles

are referred to as photons, and the energy of a single photon is given by E=hν 54.

x

y

z

E

H

x

y

z

E

H

Figure 1.3 – The wave nature of plane polarised electromagnetic radiation adapted from 55.

C

HH

SymmetricStretching

C

HH

C

HH

BendingAntisymmetricStretching

C

HH

Rocking

C

HH

Wagging

C

HH

Twisting

Figure 1.4 – Examples of vibrational modes for a methylene group

(+ and – indicates movement into and out of the page respectively).

λ

For a vibrational transition to occur, according to quantum mechanics, the energy

of a photon of the exciting radiation must be the same as the energy difference between

two discrete vibrational levels. However, not every transition is permitted according to the

selection rules. Assuming a harmonic oscillator, the two selection rules for a diatomic

molecule are: 1). the vibration of the molecule must produce a sinusoidal change in

dipole moment; and 2). the vibrational quantum number can change only by +1 54. Types

of vibration include stretching, bending, rocking, twisting and wagging (Figure 1.4).

Heteronuclear diatomic moieties (such as O-H, N-H and C=O) that have a permanent

dipole moment, have strong IR absorptions. Hence, homonuclear diatomic molecules

(such as O2, N2 and H2) that do not possess a permanent dipole moment, do not absorb

in the infrared region. Absorptions occur in regions of the IR spectrum which correlate

with specific chemical structural fragments. These fragments are then used to identify the

functional groups present within the sample matter.

1.9.2 FOURIER TRANSFORM INFRARED (FT-IR) SPECTROSCOPY

Infrared spectroscopy has developed remarkably from the earlier prism or grating

instruments with development of Fourier Transform Infrared (FT-IR) instruments due to

advantages of the latter in increased signal-to-noise and sensitivity. To fully understand

the progression towards FT-IR, and how it has become the predominant way of obtaining

IR spectra, the differences between the two instruments will be summarised. Advantages

FT-IR introduces are based upon the ‘Fellgett’ or ‘Multiplex’ Advantage and the

‘Jacquinot’ or ‘Throughput’ advantage 56; these will also be discussed.

The fundamental advancement in the development of FT-IR spectroscopic

analysis was the invention of the Michelson interferometer. This optical device was

invented in 1880 by the Nobel Prize winner, Albert Abraham Michelson and to this day

remains the vital section in FT-IR instruments 52. A Michelson interferometer creates an

interference pattern in the light source by separating the infrared beam from the source

into two components and recombining the components after a path difference is

introduced 57.

The fundamental measurement obtained from an FT-IR instrument is an

interferogram. An interferogram is a curve of output intensity versus optical path

difference (OPD) of the two resultant beams within the Michelson Interferometer 58. The

mathematical operation of converting or transforming a signal, which varies with path

length, to a spectrum in which intensity varies with wavelength, is known as the Fourier

Transform. Hence, the term Fourier Transform Infrared Spectroscopy.

The Jacquinot, or Throughput advantage, arises because – as compared to a

dispersive instrument – the energy throughput in an interferometer is higher while the

resolution is maintained 56. This increase in optical throughput arises from the absence of

spectral slits and means that the signal at the detector is higher leading to increased

signal-to-noise ratio. The Multiplex, or Felgett advantage, is based on the fact that in an

FT-IR instrument all the wavenumbers of light are detected simultaneously, whereas in

the original dispersive spectrometers only a small range of wavenumbers is measured at

a time. Thus, a complete spectrum can be obtained very rapidly and many scans can be

averaged in the same time as a single scan can be obtained by a dispersive instrument.

A further advantage associated with FT-IR is the Connes’ advantage and refers to

enhanced photometric accuracy arising from the built-in electronic calibration resulting

from a single wavelength interferogram produced by the interaction of an alignment laser

with the beam splitter 56. This calibration makes the results more accurate and

reproducible, therefore, allowing co-adding of multiple scans without misalignment.

1.9.3 NEAR INFRARED (NIR) SPECTROSCOPY

The boundaries between near-infrared (NIR) spectroscopy and mid-infrared (mid-

IR) spectroscopy are somewhat artificial 53 as NIR absorptions are attributed to

information from the fundamental mid-IR region. The overtone and combination bands

measured in the NIR region are at least one or two orders of magnitude weaker than the

absorptions in the fundamental mid-IR region 53. Hence, sample size in the mid-IR region

is generally milligrams or equivalent, while that required to measure comparable levels of

light absorption via NIR analysis is significantly larger.

An FT-IR instrument may be adapted with the use of the correct source, beam

splitter and detector to measure absorptions in the NIR region. The source generally

employed for this purpose is a quartz halogen lamp. Depending on the spectral range

and performance required, a quartz beam splitter coated with germanium and silicon is

commonly employed. However, calcium fluoride is also suitable and widely used.

Common detectors include many intrinsic semi-conducting materials such as lead sulfide,

indium antiminide and indium arsenide. These materials provide various response

characteristics and cover a variety of spectral detection ranges.

Figure 1.5 – NIR Fibre Optic Probe, Smart Near-IR FibrePort.

Standard sampling techniques employed in present day NIR spectroscopy are

reflection, transmission or transflection. For most NIR work, reflectance methods are

widely used 59. Generally any solid material which may occupy a sample cup with a

quartz window can be measured by reflectance methods. Removable accessories have

minimised sample handling leading to faster measurements and reduced possibility of

contamination. Two significant accessory advancements are the fibre-optic probe and a

quartz window.

A NIR fibre optic interface and probe give the user a unique ability to bring the

sampling accessory to the sample. This is extremely useful for analysing samples that

are at a remote location or are not the optimal size or shape to fit in a standard sample

compartment as well as the extra benefit of analysing samples in situ. Spectra can be

recorded as easily as touching the sample with the tip of the fibre optic probe; no

preparation is required. Polymers, pharmaceutical powders and tablets, liquids, foods,

fabrics and chemicals have all been analysed with fibre optic probes 51. Diffuse

Reflectance or DRIFTS (Diffuse Reflectance Infrared Fourier Transform Spectroscopy) is

the basis of many NIR Fibre Optic Probes.

1.9.4 DIFFUSE REFLECTANCE INFRARED FOURIER TRANSFORM (DRIFT)

SPECTROSCOPY

DRIFT Spectroscopy is a technique used to obtain an IR or NIR spectra from a

rough surface. Diffuse Reflectance occurs as a result of incident radiation contacting a

roughened surface such as a powder, granules or other solid material. The incident light

beam penetrates the analyte surface, interacts with the sample and exits the surface at

various angles with the scattered light being collected by an integrating sphere. The

alternative, to diffuse reflectance is known as Specula Reflectance, in which the angle of

the incident radiation is equal to the angle of the reflecting radiation. When measuring

diffuse reflectance, the resultant reflectance is influenced by the nature of the sample and

the way the sample is prepared 53. The sensitivity of a DRIFT accessory depends upon

the angle of radiation that it captures and channels to the IR detector. To prevent

embellished diffuse reflectance it is necessary to obtain a sample with uniform particle

size and minimal particle packing.

In Diffuse Reflectance, the path length is not defined and hence, the resulting

spectrum is distorted and not useful for quantitative analysis. Application of the Kubelka-

Munk equation relates the spectral response to sample concentration, transforming the

reflectance spectrum into a format that resembles an absorbance spectrum 53. The

Kubelka-Munk relationship is as follows,

( )( )

CKR

RRf

2

2

2

1=

−=

∞

∞

∞ Equation 1.1

where, R∞ = Sample reflectance spectrum at infinite sample depth ratioed

against a non absorbing sample matrix such as KBr.

K2 = Proportionality constant

C = Concentration of absorbing species.

1.10 Inductively Coupled Plasma – Optical Emission Spectroscopy

(ICP-OES)

ICP is an effective source of atomic emission which can, in principle, be used for

the quantitative determination of all elements 60. A complete multi-component analysis

can be undertaken in seconds and with the consumption of only millilitres of sample

solution. A calibration curve is established, through introduction of standard analyte

samples into the plasma with the instrumental response being linear, typically over five

orders of magnitude. Detection limits are generally quite low, for most elements falling

within the range of 1-100µg.L-1. Many wavelengths of varied sensitivity are available for

the determination of any one element, so that ICP-OES is suitable for all concentrations,

from ultra-trace levels to major components. The following discussion of ICP-OES

instrumentation and spectral interferences was written sourcing information from various

references 60-63.

1.10.1 ICP INSTRUMENTATION

Efficient introduction of the sample may be achieved using a pneumatic concentric

glass nebuliser. The Venturi effect and nebuliser are used to draw a liquid sample

through a capillary tube. Argon gas is introduced through a side arm of the nebuliser and

exits through the nozzle creating a region of low pressure. The interaction of the argon

gas and liquid sample causes the production of a coarse aerosol upon expulsion from the

nozzle. Further reduction of particle size within the aerosol is achieved through the use

of a spray chamber, commonly a double-pass chamber. The aerosol produced by the

spray chamber is now fine enough to enter the plasma. Discrete sample introduction is

advantageous to enable all of the analyte to be introduced into the plasma in a short

period of time.

The plasma is formed within the confines of three concentric glass tubes. An

initial spark from a Tesla coil initiates the plasma by acting as a ‘seed’ of electrons

allowing ionization of the carrier gas to occur. The escaping high velocity argon gas

causes air entrainment back towards the plasma torch creating a characteristic bullet

shape 61 with temperatures ranging from 6000 – 10 000K 62. The sample atoms are

released into the torch where they collide with the rapidly moving argon ions becoming

excited. As the excited sample atoms pass through the plasma, they relax to a lower

energy state emitting a photon according to the following,

νhEEEAroo +→→++ *

Equation 1.2 where, Ar – Argon Cation

Eo – Ground State Electron

E* - Excited State Electron

h – Plank’s Constant

v – Frequency

Modern detectors commonly employed are photo multiplier tubes (PMT’s) and

charge transfer devices (CTD’s). A photomultiplier is a device which converts incident

light into an electrical current. Incident light from the plasma strikes the cathode and

emits electrons which are accelerated down the dynode chain. Each time an electron

impacts a dynode, a number of secondary electrons are emitted and consequently the

signal is amplified. In this manner a single photon is responsible for generating a shower

of electrons and therefore a significant electrical signal.

A charge transfer device is an array of closely spaced metal semi-conductors

consisting of a series of cells which accumulate charge when exposed to light. Two

common forms of CTD’s are available, a charge couple device (CCD) and a charge

injection device (CID). CTD’s have similar detection limits, sensitivities and linear ranges

as a PMT while also having the capability of measuring the background signal

simultaneously 63.

1.10.2 ICP SPECTRAL INTERFERENCES

Spectral interferences for atomic emission spectroscopy can be classified into two

main categories, spectral overlap and matrix effects.

Spectral Overlap

Three types of spectral overlap exist; Direct Spectral Overlap, Wing Overlap and

Background Shift. Spectral and Wing Overlap can occur as a result of an interfering

emission line from another element, the argon gas or impurities. Direct Spectral Overlap

is generally overcome by selecting another wavelength for measurement. Elimination of

Wing Overlap requires the instrument resolution be increased. Failing these, a

mathematical model may be applied. Background shift may only be corrected by

accurate measurement of the background on either side of the wavelength of interest 61.

Matrix Interferences

Matrix interferences are associated with how an instrument responds to a given sample

matrix and isolates the analytes of interest 63. The transport efficiency from the spray

chamber is dependent upon the viscosity, surface tension, vapour pressure and density

of the liquid sample. Calibration with the use of standards having the same matrix as the

sample reduces such effects. Internal standards and standard addition can also help to

minimise matrix interferences.

1.11 X-Ray Diffractometry

1.11.1 XRD CONCEPT

English physicist W. L. Bragg, observed that reflected X-ray beams give

detectable maxima only when the differences in the distance travelled are equal to the

integral multiple of wavelength, λ 64. In a quantitative interpretation of this phenomenon,

Bragg, instead of the incident angle, used its complementary or reflective angle, θ.

Hence radiation with wavelength, λ, is reflected from a set of planes with d-spacing only

at conditions, when

nλ = 2dhkl sin θ Equation 1.3

and n is an integer and hkl is the miller index of the plane 64. This equation is derived by

a high degree of simplification of the diffraction phenomena and is frequently employed in

X-ray diffraction analysis 65.

1.11.2 XRD INSTRUMENTATION

Powder diffractometry is mainly used for the identification of compounds by their

diffraction patterns 66. The instrumentation required for X-ray powder diffractometry

consist of three basic sections; a source of radiation, diffractometer and detector or

counting equipment. These three basic sections will be discussed below from

information sourced from various references 64-68.

Source of Radiation

A stable source of radiation is produced by the generator and the X-ray tube. An X-ray

tube consists of a tungsten filament and a specific anode to produce particular and

monochromatic radiation. Current passes through the tungsten filament causing it to

glow and electrons to be emitted. These electrons are accelerated towards the anode by

means of a high potential, usually in excess of 30 kilovolts 65. Electrons are converted to

X-rays in a relatively inefficient process with the majority of electron energy lost as heat.

To reduce this heat, and consequently the energy lost, the upper section of the X-ray

tube is constructed from copper and the anode is water cooled. A filament is located in a

Wehnelt cylinder to ensure a finely focused beam of electrons. The chamber is

evacuated to ~10-6mmHg and sealed. X-rays are produced at the anode and allowed to

pass out of the tube via beryllium windows.

Diffractometer

The instrumental setup known as Bragg-Brentano focusing geometry is frequently used in

modern instruments. The diverging X-ray beam from a line source falls onto the

specimen, is diffracted and passes through a receiving slit into the detector. The amount

of divergence is determined by the effective focal size which is matched with the scatter

slit 67. Lateral divergence is controlled by two sets of parallel plate vertical collimators

placed between focus and specimen, and specimen and scatter slit respectively 65. The

instrumental line width of a diffracted line profile will be determined mainly by the angular

aperture of the receiving slit, but the intensity of this line will be dependent upon both slit

aperture and the focal spot characteristics of the X-ray tube.

Detector

The function of the detector is to convert the individual X-ray photons into voltage pulses.

The voltage pulses are then integrated by the counting equipment giving various forms of

visual indication of X-ray intensity. Detectors used in conventional X-ray powder

diffractometers are either one of two main types, gas counters or scintillation counters.

Gas Counter – A gas counter is based on the principle that when an X-ray photon

interacts with an inert gas atom, the atom may be ionised in one of its outer

orbitals giving an electron and a positive ion. The counter itself consists of a

hollow metal tube carrying a thin wire along its radial axis. The wire is essentially

the anode with a tension of between 1.5 – 2kV. Each electron produced gives

rise to many secondary electrons leading to an effect called gas amplification. A

burst of electrons reaching the anode causes a decrease in voltage at the

condenser which is passed through cathodes and amplifiers to the scaling

circuitry.

Scintillation Counter – In the scintillation counter the conversion of the energy of X-ray

photons into voltage pulses is a double stage process. Firstly, the X-ray photons

are converted into flashes of blue light by means of a phosphor. A phosphor is a

substance that is capable of absorbing radiation at a certain wavelength and re-

emitting it at a longer wavelength. The second stage involves the blue light being

converted to voltage pulses by means of a photomultiplier. The light photons fall

onto a photocathode producing a burst of electrons which are focussed onto a

series of dynodes amplifying the signal. The electrons are then collected by the

anode and a voltage pulse is formed in a similar way to that already described for

the gas counter.

In the powder method, the crystal to be examined is reduced to a very fine powder

if not already in the form of loose microscopic grains. The sample is placed in a suitable

holder and in direct contact with the monochromatic X-rays. Each particle of the powder

is a tiny crystal, or an ensemble of smaller crystals oriented at random with respect to the

incident beam. The crystals are randomly arranged in every possible orientation and

there is a sufficient number of planes oriented at the Bragg angle, θ, for diffraction to

occur. The possibility of the reflection is the same for all the planes lying in the path of the

primary beam. The diffracted beam is detected using the Bragg-Brentano’s focussing

principle discussed earlier. The result of the evaluation of a diffractogram of an unknown

sample is a set of interplanar spacings, dhkl, with corresponding intensities. On the basis

of this data, the composition of the sample can be determined. This must be performed

by comparing the resultant diffraction pattern with a series of reference diffractograms in

order to find a reference identical to the unknown. Various databases are available for

comparison with unknown diffractograms.

Chapter Summary

This chapter has served as an introduction to the background of soils, their

formation, classification, horizons, sampling techniques and relevance in forensic

science. The theory associated with the various methods of analysis available for

examining soil samples has been explored, with an emphasis on ICP-OES, XRD and

Vibrational Spectroscopy, specifically NIR and DRIFT Spectroscopy.

To reiterate, the aim of this project is to research the possibilities of bringing

together NIR spectroscopy and Chemometrics to establish whether this combination can

discriminate or match soil samples for use in a forensic investigation.

The following chapters will detail the analytical procedures, chemometric methods

and a detailed discussion of the results obtained from the study. The thesis will conclude

with suggestions for future work that may contribute to advancing the use of NIR

spectroscopy in the Forensic Science discipline.

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1991, Chichester: Ellis Horwood Ltd.

59. Wetzel, D.L., Near Infrared Reflectance Analysis; Sleeper Among Spectroscopic

Techniques. Analytical Chemistry, 1983. 55(12): p. 1165-1176.

60. Thompson, M. and J.N. Walsh, Handbook of Inductively Coupled Plasma

Spectrometry. 1989, Glasgow: Blackie & Sons.

61. Dean, J.R., Practical Inductively Coupled Plasma Spectrometry. 2005, Chichester:

John Wiley & Sons.

62. Robinson, J.W., Atomic Spectroscopy. 1990, New York: Marcell Dekker.

63. Hill, S.J., Inductively Coupled Plasma Spectrometry & Its Applications. 1999,

Sheffield: Sheffield Academic Press.

64. Pungor, E., A Practical Guide to Instrumental Analysis. 1995, Florida: CRC Press.

65. Jenkins, R. and J.L. De Vries, An Introduction to X-Ray Powder Diffractometry.

1997, Eindhoven: NV Phillips Gloeilampenfabrieken.

66. Hammond, C., The Basics of Crystallography & Diffraction, 2nd Edition. 2001,

New York: Oxford University Press.

67. Warren, B.E., X-Ray Diffraction. 1969, Massachusettes: Addison Wesley

Publishing Company.

68. Cuillity, B.D. and S.R. Stock, Elements of X-Ray Diffraction, 3rd Edition. 2001,

New Jersey: Prentice Hall.

2.0 ANALYTICAL PROCEDURES

2.1 Collection and Preparation of Soils

The soils employed in this study were previously collected by Al-Shiekh Khalil et

al. 69. They were taken from within the boundaries of Logan City, South-East

Queensland, Australia, and classified (Appendix Table 7.1) on the basis of their type and

properties according to the Australian Soil Classification System 24, 25. This classification

process includes 14 orders (Table 1.1).

For this project, each sample was stored in a plastic clip-seal bag containing two

smaller clip-seal bags and labelled according to the sampling site and soil horizon. One of

the two smaller bags contained raw soil, displaying the original soil characteristics, and

the other bag contained a crushed homogenised sample. The raw sample was used in

this study to ensure that (a.) all samples were treated uniformly throughout preparation

and analysis, (b.) preparation was completed according to the outcomes of this specific

project and (c.) to avoid contamination between implements and apparatus.

All soils were oven dried at 105oC for 2 hours before they were ground and

separated into the various particle sizes by the standard sieving methods. Grinding was

carried out either by hand grinding with a mortar and pestle or by a mechanical ring

grinder. The <150µm fraction was transferred to a fresh clip-seal plastic bag ready for

use in further analysis. This fraction was selected as it is known to contain a high

proportion of heavy minerals, micas and clay minerals, within which many of the trace

elements are concentrated 70. Final homogenization of the soils was achieved with the

use of a stainless steel spatula and/or mixing within the packet on various occasions,

especially prior to any further sub-sampling. Samples which weighed over ca. 100g were

split to obtain a representative sub-sample.

2.2 Materials and Methodology for X-ray Diffraction Analysis

XRD analysis was employed to define the mineralogy of each soil, to identify and

quantify the dominant clay minerals present as well as to determine the amorphous

content in each sample. Moore & Reynolds define a ‘clay mineral’ as the relatively small

number of minerals that occur as grains and are less than 2µm in size 71. The selection

of soils with specific characteristics for subsequent analysis will be assisted by the

knowledge of their mineralogy obtained through the XRD methods.

Sample preparation is an important requirement for accurate analysis of soils by

XRD. This is especially important for soils that contain finely divided colloids, which are

poor reflectors of x-rays, as well as other materials such as iron oxide coatings and

organic materials. Appropriate sample preparation techniques for soils have been

described by Moore and Reynolds and are designed to remove undesirable substances

as well as to obtain desirable particle size, orientation and thickness 71.

In this work, each of the soils, previously treated according to Chapter 2.1, were

mixed with the use of a stainless steel spatula. Those soils with a sample mass of

greater than ca. 50g were split to attain a representative sub-sample. XRD analysis

requires the soils to consist of extremely fine grains to achieve good signal-to-noise ratio,

avoid spottiness and minimise preferred orientation. Conversely, excessive dry grinding

can result in lattice distortion and changes of phase. Formation of an amorphous layer

around individual grains has been known to occur as a result of excessive grinding 72. In

extreme cases, this can lead to strains on the crystal structure that cause XRD line

broadening or the production of X-ray amorphous material 71. Thus, sample preparation

aimed to improve diffraction characteristics of the sample and to promote dispersion

during size fractionation. It was important to treat all soils in the same manner, as

described in Chapter 2.2.1 below.

2.2.1 AIR DRIED AND GLYCOLATED THIN FILM PREPARATION

Approximately 1g of sample was placed into a specimen vial and filled with 0.5%

Calgon in distilled water. The vial was sealed and shaken briefly by hand. The solution

was sonicated for 30 seconds and allowed to settle. The vial was then shaken

vigorously, by hand, for 30 seconds and allowed to settle for 5 minutes. The supernatant

liquid was transferred by pipette onto a copper slide and allowed to dry overnight. The air

dried slides were mounted in sample holders, loaded into the instrument and the XRD

analysis was performed. After the air dried analysis was complete, all slides were treated

with ethylene glycol (8% in ethanol), allowed to dry and analysed using the instrument

parameters outlined in Chapter 2.2.3.

Clay samples are typically analysed under air dried conditions. The sample slides

are treated under ethylene glycol-solvated conditions to expand the mixed-layer clays

and the XRD analysis repeated. If expandable clay minerals are present, the d-spacing

is shifted after glycolation as the treatment expands the crystal lattice. Smectite is a clay

mineral which is susceptible to expansion as the polar organic compound enters the

interlayer space. The computer software HIGH SCORE® was used to interpret these

results and identify the phases present.

2.2.2 POWDER PREPARATION

An analytical balance was used to accurately weigh 2.7g of soil sample and 0.3g

of the internal standard Corundum (Aluminium Oxide). This 3.0g weight aliquot was

transferred to a micronising tube with the use of ~10cm3 of ethanol. The tube was placed

in a Micron Sing Mill and micronised for 6 minutes to reduce the soil particle size. The

soil, ethanol mixture was transferred to a large beaker and the micronising tube rinsed a

further 3 times with ethanol in the Mill to ensure complete transferral to the beaker and to

assist in cleaning the tube between samples. The combined soil/ethanol washes were

allowed to evaporate overnight in an oven set to 55oC. The micronising tube was cleaned

between samples by micronising with a wash of distilled water, followed by a detergent

wash, another distilled water wash and a final ethanol wash.

The following day, the slides were prepared by transferring the dried powder from

the beaker onto a slide and applying pressure to compact it. A razor blade was used to

smooth the surface and ensure the correct surface height. Each slide was mounted into

the sample holder and XRD analysis was performed. The computer software

SIROQUANT 2.5® was used for quantification.

2.2.3 XRD INSTRUMENT PARAMETERS

The instrument used throughout the XRD analysis was a PANalytical XPERT-

PRO X-Ray Diffractometer fitted with a X’Celerator RTMS Detector and the following

instrumental parameters,

X-Ray Tube Anode Material Cu

Kα (Å) 1.5418

Voltage (kV) 40

Current (mA) 40

Soller Slit (rad.) 0.04

Anti-scatter Slit AS Slit 3.4mm (X’Celerator) Fixed 1O

Divergence Angle 0.5 O

Rotation Time (s) 2.0

Monochromator Diffracted Beam Flat 1x graphite Cu

Scan Range 3.5000 – 75.0075 O

2.3 Materials and Methodology for Inductively Coupled Plasma Analysis

The sample preparation for ICP analysis was performed following a Lithium

Metaborate fusion procedure similar to that described by Pye et al. 70 for the elemental

analysis of soil samples for forensic purposes.

It is often desirable to analyse a standardised particle size fraction rather than the

bulk sample for purposes of forensic comparison of soils. The soils studied in the ICP

analysis had been previously pre-treated as outlined in Chapter 2.1 so as to achieve

particle size uniformity and homogeneity. All soil samples, calibration standards and

control standards were dried at 105oC for 1 hour and stored in a vacuum desiccator. The

control standards used in this section were NIST SRM Buffalo River Sediments (SRM

2704) and were prepared under the same conditions, in the same manner as the

samples. A series of five calibration standards, including one blank, were prepared and

used to calibrate both instruments.

2.3.1 LITHIUM METABORATE FUSION

Each soil was accurately weighed (0.2000 + 0.0005g) into a platinum crucible.

Subsequently, Lithium Metaborate flux (1.2000 + 0.0005 g) was accurately weighed into

the platinum crucible with the soil and the two substances were mixed. The mixtures

were fused over the flame produced by a high capacity Primus Cyclone 8277 torch.

Throughout this process the mixtures were swirled using platinum tipped tongs to grasp

the crucible. The fusion was complete when a clear ‘glass like’ film could be seen

covering the base of the crucible. The crucibles were allowed to cool. A solution of 1:3

AR Nitric Acid (20mL) was added to each crucible. Sample dissolution was achieved with

the aid of a magnetic stir bar and heating to ~80oC for about 30 minutes or until

dissolved. Hydrogen Peroxide (1-2 drops) was added to those samples containing a

slight precipitate. The solution was transferred to a 200mL volumetric flask and made up

to the mark with deionised water. The blank, duplicates, calibration standards and control

standards were all treated in this same manner.

2.3.2 ICP INSTRUMENT PARAMETERS

The ICP analysis was performed by completing three runs on two separate

instruments. The first instrument used was a VARIAN LIBERTY ICP-AES. This

instrument was used to determine the phosphorus oxide (P2O5) concentration for all

samples, standards and CRM’s using the following parameters;

Replicates 2

Power (kW) 1.45

Plasma Flow (L/min) 15

Auxiliary Flow (L/min) 0.75

Pump Speed (rpm) 17

Viewing Height (mm) 10

Nebuliser (kPa) 240

Stabilization Time (sec) 10

Rinse Time (sec) 10

Background Correction Dynamic

The optics were purged with Argon gas to allow for the determination of Phosphorus at

the wavelength 177.495 nm.

The other instrument was a VARIAN VISTA-MPX CCD Simultaneous ICP-OES.

Two analysis programs were constructed, the first for the analysis of the major oxides;

Al2O3, CaO, Fe2O3, MnO, SiO2, and TiO2, and the other for the analysis of the minor

oxides; K2O, BaO, MgO, Na2O and SrO. The following instrument parameters were

employed;

MAJOR ELEMENTS MINOR ELEMENTS

Replicates 3 3

Power (kW) 1.35 1.05

Plasma Flow (L/min) 13.5 13.5

Auxiliary Flow (L/min) 0.75 0.75

Pump Speed (rpm) 18 20

Viewing Height (mm) 6 5

Stabilization Time (sec) 15 15

Rinse Time (sec) 10 10

The optics were purged with Argon gas to allow determination of the elements at

the wavelengths listed below 61;

MAJOR ELEMENTS (nm)

Al 394.401

Ca 317.933

Fe 259.940

Mn 257.610

Si 251.611

Ti 336.122

MINOR ELEMENTS (nm)

Ba 455.403

K 766.491

Mg 285.213

Na 588.995

Sr 407.771

P 177.495

2.4 Materials and Methodology for the Initial Investigation of NIR

Spectroscopy for the Discrimination of Soils

A total of 50 spectra were collected from the 25 soils discussed in Chapter 2.1.

Duplicate spectra were collected from all soils. The instrument used was a Nicolet

NEXUS EURO FT-IR Spectrometer fitted with a Smart Near-IR FibrePort Accessory with

the following parameters;

Number of Scans 256

Resolution (cm-1) 16

Gain 8

Aperture (µm) 89

Mirror Velocity (cm.s-1) 1.2659

Source White light

Detector TEC NIR InGaAs (8000-4000cm-1)

Beamsplitter Quartz (15 000-2000cm-1)

The optical fibre accessory was held secure with a clamp and retort stand. Each

soil sample was simply placed onto the quartz window and the spectra recorded. A

background spectrum was recorded between each sample analysed. The spectra were

saved individually as .SPA files with the use of the OMNIC E.S.P. 5.2a Spectral Software

Program®. Spectra were recorded in transmittance, over a range of 10 000 - 4000cm-1.

The OMNIC .SPA files were imported into the spectral software package

GRAMS/32AT 6.0® (Galactic Industries Corporation, Salem, NH, USA) as GRAMS

SPECTRAL .SPC files. The spectra were converted from transmittance to absorbance

and to second derivative profiles (Savitsky-Golay, 5 points) through the macro option

available in the GRAMS software package. The macro was also used to interpolate or

decrease the data point density of each file by averaging two data points to one. This

data reduction step made it possible to transfer the matrix to a Microsoft Excel 5.0

spreadsheet .XLS file. The final transferral of data was to import the absorbance, 2nd

derivative spectra into the commercially available Chemometrics software package for

multivariate analysis, SIRIUS version 7.0© (Pattern Recognition Systems AS, Bergen,

Norway). Once imported into this program, the matrix was pre-treated using autoscaling.

Auto-scaling is an extension of mean-centering. Essentially removing the absolute

intensity information (through mean-centering) plus removing total variance information

(through standardising) for each of the variables. Auto-scaled data has a unique

characteristic that each of the variables has a mean of zero and a standard deviation of

one.

2.5 Materials and Methodology for Quartz-Kaolinite Comparison by

NIR and Chemometrics

From the soils previously discussed (Chapter 2.1), three high quartz, low kaolinite

soils (Table 2.1) were selected. Similarly, three kaolinite samples (Table 2.2) were

Table 2.1 – Properties of the high quartz soils used for mixing with kaolinite.

Sample Number

Site Number

Horizon %

Quartz %

Kaolinite

4870-2 2 A 89.9 2.2

4870-4 5 A 77.5 4.2

4870-12 18 A 73.9 6.1

Table 2.2 – Properties of the kaolinites used for mixing with high quartz soils.

Name Origin Crystallinity Supplier

KGa-1a Georgia Well crystallised

Source Clay Minerals Repository, University of Missouri, USA.

API #9 Mesa Alta, New Mexico Not stated Ward's Natural Science Establishment

Inc, Rochester, NY.

KGa-2 Georgia Poorly crystallised

Source Clay Minerals Repository, University of Missouri, USA.

obtained, two of which were a fine powder. The other kaolinite, KGa-2, was in solid form,

requiring crushing with the Mechanical Ring Grinder and sieving through the ≤150µm

sieve. The finely powdered soils and kaolinites were mixed in such a way that each soil

was mixed with each kaolinite, and vice versa, in varying ratios (according to Table 2.3).

The ratio of kaolinite to high quartz soil was calculated according to mass, and the

combination was homogenised in a mortar and pestle before a final sieving. The high

quartz soil-kaolinite mixtures were placed in plastic clip-seal bags for storage and easy

transportation.

Table 2.3 – Ratios of each kaolinite mixed with each high quartz soil.

Kaolinite KGa-1A

Kaolinite API #9

Kaolinite KGa-2

100% 100 % 100% 100 % 100 % 100 % 100 % 100 % 100 %

85:15 90:10 75:25 75:25 75:25 85:15 85:15 75:25 75:25 75:25 80:20 60:40 60:40 60:40 75:25 75:25 60:40 60:40 65:35 70:30 45:55 45:55 45:55 65:35 65:35 45:55 45:55 55:45 60:40 30:70 30:70 30:70 55:45 55:45 30:70 30:70 45:55 50:50 15:85 15:85 15:85 45:55 45:55 15:85 15:85

35:65 40:60 35:65 35:65

25:75 30:70 25:75 25:75

15:85 20:80

100% Soil

4870-12

100 % Soil

4870-2

100 % Soil

4870-4 15:85 15:85

100 % Soil

4870-4

100 % Soil

4870-12

10:90 100 % Soil

4870-2

100 % Soil

4870-12

100 % Soil

4870-2

100 % Soil

4870-4

Triplicate NIR spectra were collected from each of the mixed species as well as

the raw soils and kaolinites (Instrument, parameters and processing were consistent with

Chapter 2.4). A total of 240 spectra were recorded over a range of 10 000 - 4000cm-1.

2.6 Materials and Methodology for Temperature Dependent NIR Analysis

From the original 25 soils pre-treated according to Chapter 2.1, 6 soils were

selected to represent 6 sampling sites, 3 horizons and various soil types (Appendix Table

7.1). A summary of this information is outlined in Table 2.4.

Figure 2.1 – Soil 4870-26 ‘Backyard Soil’ (left) and 4870-27 ‘Melcann® Sand’ (right) in

blue tidy tray containers used for the forensic scenario simulation.

a.)

b.)

c.)

Figure 2.2 – The three shoes used in the forensic scenario simulation

a.) Walk Shoe, b.) Jogger, and c.) Leather Shoe.

Table 2.4 – Australian Soil Classification information for the soils used in the

temperature dependent NIR analysis.

Sample Number

Site Number Horizon Soil Type

4870-6 8 A Yellow Dermosol 4870-12 18 A Yellow Kandosol 4870-18 33 B2 Red Sodosol 4870-20 38 B1 Rudosol 4870-22 42 B2 Grey Kurosol 4870-25 48 B1 Brown Chromosol

The apparatus involved attaching heating accessories to the previously described

Nicolet NEXUS EURO Near FT-IR Spectrometer. A LINKAM THMS 600 Heating Device

connected to a LINKAM TMS 93 Digital Heating Control was used to control the

temperature and ramp speed. The heating device was cooled with water and flushed

with nitrogen gas to remove excess oxygen and moisture. The 360N SABIR optic fibre

accessory was held in contact with the quartz window of the heating device using a retort

stand. The sample compartment was filled with soil and placed inside the heating device.

Spectra were collected at room temperature, then at 100oC and in 100 degree increments

until 600oC was reached. Duplicate spectra were collected at all temperatures.

A total of 84 spectra were collected from the 6 soils at the varying temperatures,

over the range of 10 000 – 4000cm-1. The instrument, parameters and spectral

processing remained as those previously described in Chapter 2.4.

2.7 Materials and Methodology for the Simulation of a Possible Forensic

Application

2.7.1 DRY BRUSHED METHOD

Commercial Gap Sand, MELCANN® was purchased from a local hardware store

and the soil sample was collected from the backyard of an inner Brisbane property. Both

the sand and soil were weighed (8kg each) into separate blue plastic tidy tray containers.

They were spread evenly in the tray to achieve an average soil or sand thickness of 2-

3cm (Figure 2.1).

The tray containing the sand was placed on the ground and a person wearing

joggers placed their foot onto the surface of the sand and transferred their weight

removed from the foot and placed over an A3 sheet of paper. Using a clean and dried

paint brush, the loose sand was brushed off the jogger and onto the surface of the paper.

The paper was then funnelled and the sand particles transferred to a 50 x 25mm soda

glass specimen tube and sealed with a plastic stopper. Using a stainless steel spatula,

two reference sand samples (fore foot and rear foot) were collected from the impression

remaining in the sand for comparison with that already collected. These samples were

also transferred to a soda glass specimen tube and sealed for storage and transportation.

The process was repeated maintaining the sand but altering the shoe which contacts the

sand. Three different shoes were used (Figure 2.2) with three samples from each

simulation being collected. Hence, a total of 9 sand samples were collected using the dry

method.

Once the three shoe types had been utilized in the simulation with sand, the

medium was changed from the sand to the soil and the process repeated in full. As the

soil appeared to be more heterogeneous than the sand, it was decided to collect more

than the two (ie. fore foot and rear foot) soil samples from the impression. A total of five

soil samples were collected from the impression and the location of these sampling sites

recorded on a diagram of the shoe sole. Each of these collected samples were labelled

with letters, A through to E (See Appendix Table 7.2 for sample details). All three shoe

types were applied in the simulation using soil as the surface medium. A brushed soil

sample plus the 5 impression surface samples made a total of 6 samples collected for

each shoe. With the three shoes, a total of 18 dry soil samples were collected and stored

in the specimen tubes.

Duplicate NIR spectra were obtained from each of the collected dry samples

according to the instrument parameters and processing methods previously described in

Chapter 2.4. A background spectrum was recorded between each sample analysed.

Refer to Appendix Table 7.2 for file names and sampling details.

2.7.2 WET SAMPLED, OVEN DRIED METHOD

A quick and simple experiment was conducted in order to calculate a suitable

volume of water per unit mass of soil and sand sample to achieve a ‘muddy’ texture for

both soil and sand. A 250g sample of the sand along with a 250g sample of the soil were

weighed into individual beakers. Small equal quantities of water were added to each and

stirred manually with a stainless steel spatula until a suitable moisture content was

achieved for both. It was concluded that for every 250g of solid, 50mL of water was

Figure 2.3 – Impression remaining on the surface of the soil from contact of the Walk

Shoe with the Backyard Soil (15cm metal ruler included for scale).

Figure 2.4 – Wet Backyard Soil adhering to the Walk Shoe sole after

contact with the soil.

necessary to achieve the ‘muddy’ texture. This ratio was simply scaled up and the volume

of water required for 8kg of solid was found to be 1600mL. The volume of water added to

the sand and soil was equal, and both were stirred equally with a large stainless steel

spatula.

The blue tidy trays with either sand or soil were used again in this section. To

each blue container 1600mL of deionised water was added and the mixture stirred until

an even consistency was achieved. Beginning with the soil tray, the process outlined in

Chapter 2.7.1 was repeated with the person stepping onto the soil. However, in this case

the soil adhered to specific sites of the shoe (Figure 2.3 and 2.4) enabling direct sample

collection of a specific location on the shoe and corresponding site in the impression. A

total of five site specific samples were collected from the shoe and the corresponding

sites in the impression, i.e. ten samples were collected from each separate shoe contact.

The specific sites were recorded on a diagram of each shoe sole and labelled A through

to E (Appendix Table 7.3 for sample details and labels). A total of 30 soil samples were

collected from the three different shoe contacts. The process was repeated for the sand

and three shoe types with a further 30 sand samples being collected. All collected

samples were dried in an oven at 105oC for 2 hours 43. Duplicate NIR spectra were

collected using the instrument parameters and processing methods consistent with those

detailed in Chapter 2.7.1.

2.8 Chemometrics and Multi-Criteria Decision Making Theory and

Techniques

The term Chemometrics was first used in 1971 to describe the growing use of

mathematical, statistical, and other logic based methods in the field of chemistry and

particularly in analytical chemistry 73. The application of Chemometrics has found

considerable success in three general areas: (1) the calibration, validation and

significance of analytical measurement; (2) the optimization of chemical measurement

and experimental procedures; and (3) the extraction of the maximum chemical

information from analytical data. Chemometrics has developed into a highly valuable tool

offering multivariate data reduction and exploration procedures that are capable of

yielding chemical information not previously available to the analyst 74. The fundamental

goal of this section is to describe the concepts of multivariate data analysis, supervised

learning, unsupervised learning and pattern recognition techniques. The

methods which will be discussed are principal component analysis (PCA), fuzzy

clustering (FC) and SIMCA as well as PROMETHEE and GAIA.

The spectra obtained in this study can be represented by a data matrix where the

rows (objects) account for spectra of different samples (or in many cases duplicate

samples) and the columns (variables) are the spectral wavenumbers and contain the

spectral intensities of the objects.

2.8.1 PRE-TREATMENT METHODS FOR THE RAW DATA MATRIX

Pre-treatment methods are an important part of processing the raw data before

chemometrics analysis is performed. Pre-treatment or Pre-processing, as it is often

called, is defined as ‘the use of any mathematical manipulation of data performed prior to

the primary analysis’ 75. Pre-treatment is usually performed to remove non-chemical

biases from the spectral information (e.g. scattering effects due to surface

inhomogeneities, interference from external light sources, random noise) and prepare the

data for further processing 76, 77. Mathematical treatments that compensate for scatter-

induced baseline offsets include multiplicative scatter correction (MSC), Savitzky-Golay

derivative conversion and standard normal variate (SNV) correction. Other pre-treatments

include mean centring, standardisation or the combination of these two, known as auto-

scaling. Another pre-treatment method commonly used is normalisation.

X-Mean Centring is achieved by subtracting the mean of each row, x , from each

element in that row. Furthermore, the mean of each column, y , may be subtracted from

each element in that column to achieve Double Mean Centring. In other words, Double

Mean Centring is achieved by subtracting the mean of that variable vector from all of its

elements in both the x (objects) and y (variables) directions. Double Mean Centring is

performed on matrices to remove the common size effects from both variable and object

data, thus removing common features in the data leaving only interaction and noise. The

contributions of each variable, specific to each object are therefore enhanced.

Standardisation or variance scaling, is achieved by dividing each element in a

given column by the standard deviation of that particular variable column. The purpose of

this pre-treatment method is to remove the weighting that is artificially imposed by the

scale of the variables 75. This pre-treatment technique is useful as many data analysis

tools place more influence on those variables with broader ranges. In PCA, variables

with a large variance will generally have large loadings. This bias can be avoided by

standardising the data matrix to give each column a variance of one. Thus, all variables

will have the same influence on the PC model with a standard deviation of 1.

Normalisation of data requires all absorption values for an object be added to give

a total absorbance for the spectrum and then this value is divided into the individual

absorption for each wavenumber. Normalisation algorithms can be used to compensate

for baseline shifts and intensity variations between spectra resulting from path length

differences. Double Mean Centring and Normalisation are not performed in conjunction

with one another as the two techniques tend to cancel the effects of one another.

2.8.2 PRINCIPAL COMPONENT ANALYSIS (PCA)

PCA has been described as ‘an unsupervised multivariate technique in which new

variables are calculated as linear combinations of the old ones’ 78. The original variables

are transformed into orthogonal components and referred to as Principal Components

(PC’s) or latent variables. The transformation is performed with minimal loss of

information, where each PC is a linear combination of the original variables, and it

accounts for a certain amount of data variance according to the following equation 79,

pp XXXPC12121111

.... ααα +++= Equation 2.1

Where, α refers to the weights or loadings for each variable within this PC. These terms

are unique to each PC as they are functions of the angles between the variables and the

component in p dimensional space 79.

Significant information is retained in relatively few PC’s. PC1 explains the

greatest amount of data variance and the following PC’s decrease in the amount of

explained variance. In this way it is possible to display information by plotting the scores

of the first two or three PC’s. The scores can be plotted giving a two or three dimensional

view of the objects and their relative positions. The corresponding loading vectors give

the contribution (loading) of each variable to the corresponding component and may,

therefore, be used for interpretation of groups of objects with similar characteristics. In

addition, a biplot is a representation of the PC loading vectors superimposed on the score

plot so as to illustrate the relationships between objects and variables.

2.8.3 FUZZY CLUSTER (FC) ANALYSIS

In conventional data classification a given object is considered to have

membership in a single class only. Conversely, the membership of that object to all other

classes is collectively zero. The fuzzy clustering (FC) approach is non-parametric,

allowing objects to have membership in more than one class. For this reason, it makes it

possible to identify objects that display characteristics of more than one class based on

their measured properties. It is also possible to obtain information regarding the strength

of an objects association with a particular cluster 73. The degree of membership is

designated with the aid of a membership function, for example 80,

m(x) = 1-c|x-a|p Equation 2.2

where a, c and p are constants. The user is required to nominate the number of classes

and the result is a table in which a membership value, of 1 or less, for each class is

assigned for each object. The sum of the membership values for each object over all

classes is equal to 1. Membership values close to 1 represent strong belonging to that

class. Values less than 1/n (where n is the number of clusters specified by the user)

have little or no association with that class. It is also necessary for the user to nominate a

value for the weighting component, p, in the previous equation. Suitable values of p are

between 1 (hard) and 3 (soft) where higher values favour fuzzy membership. Therefore,

if an object has a high membership value when the value of p is large, then this object

has a strong association with that class 81. The major advantage of fuzzy cluster analysis

is that it facilitates the distinction between objects that clearly belong to one cluster and

those that are members of several clusters 82. On this basis, objects which are members

of a particular class may be examined without the influence of fuzzy members.

2.8.4 PROMETHEE AND GAIA

PROMETHEE is an acronym for Preference Ranking Organization Method for

Enrichment Evaluations. PROMETHEE is a non-parametric method applied in Euclidian

space to rank objects 83. GAIA, Graphical Analysis for Interactive Assistance, makes use

of PCA to support PROMETHEE as a descriptive complementary technique 84.

PROMETHEE and GAIA together, belong to a family of Multi-Criteria Decision Making

(MCDM) methods known as outranking methods. These methods are based on the

principal of pair-wise comparison which requires the user to nominate,

- A preferred ranking order, i.e. maximise (top-down) or minimise (bottom-up)

- A preference function, P(a,b), which defines how one object compares with

another

- A threshold value/s, if required by the selected preference function, to

establish how preference values relate to the preference function, and

- A weighting – set by default as 1 but may be altered - to reflect the importance

of one criterion over another.

The stepwise procedure involved in the PROMETHEE application is outlined below,

1. The raw data matrix is first transformed into a difference matrix, d, where the

column entries for each criterion are compared pair-wise by subtraction in all

possible combinations.

2. The difference value, d, is compared to a threshold, z, according to the

selected preference function P(a,b). A preference value is allocated for each

difference, d, resulting in a preference index matrix.

3. A global (overall) preference index, π, is calculated for each object by

summing all preference indices for each criterion to indicate the preference of

one object over another.

4. Out-ranking flows, Φ+ and Φ–, are calculated from the π-global preference

indices where Φ+ indicates how an object outranks all others and Φ–

expresses how an object is outranked by all others. The higher the Φ+ and the

lower the Φ–, the higher the preference for an object.

5. Out-ranking flows of a and b are then compared pair-wise according to three

rules to produce a PROMETHEE I partial ranking or partial pre-order of the

objects 85,

I. a outranks b

II. a is indifferent to b

III. a cannot be compared with b

6. The net out-ranking flow, Φ, is also calculated by eliminating the rule where a

cannot be compared to b to remove partial pre-order and establish

PROMETHEE II complete ranking. The results are displayed in an uni-

dimensional ranking, which is convenient but less reliable due to loss of some

information.

GAIA is an exploratory procedure that presents PROMETHEE II complete ranking results

in a graphical display. GAIA facilitates the interpretation of relative locations of actions,

significant criteria and the π-decision axis. The GAIA matrix is constructed by

mathematical decomposition of the net out-ranking flows 85, such that the actions may be

regarded as objects and the criteria as variables. The data is then processed by a PCA

algorithm and displayed on a GAIA biplot, illustrating the decision axis and the distribution

of objects and criterion vectors. The display may be interpreted conventionally as a

normal PCA biplot, indicating relationships between objects, variables as well as objects

and variables. The decision axis indicates the preferred action and the length of the axis

expresses the degree of decision power. An important difference between GAIA and

PCA is its capability to model scenarios based on the choice of individual preference

functions for each criterion, the choice of ranking sense (maximise/minimise) and criteria

weights. The decisions made by the user are reflected in the distribution of the criteria

vectors on the biplot, therefore making it possible to test different experimental

hypotheses by testing different scenarios.

The discussed Chemometrics methods are employed later in this thesis (Chapters

4 & 5) to provide further interpretation of the analytical spectral data collected as a result

of this study.

References

69. Al-Shiekh Khalil, W.R., Integrated Land Capability for Ecological Sustainability of

On-Site Sewage Treatment Systems, in School of Civil Engineering 2005,

Queenland University of Technology: Brisbane. p. 293.



71. Jacquier, D.W., et al., The Australian Soil Classification; An Interactive Key. 2000,

Collingwood: CSIRO.

72. Pye, K., S.J. Blott, and D.S. Wray, Elemental Analysis of Soil Samples for

Forensic Purposes by Inductively Coupled Plasma Spectrometry - Precision

Considerations. Forensic Science International, 2006. 160: p. 178-192.

73. Moore, D.M. and R.C. Reynolds, X-ray Diffraction, Identification and Analysis of

Clay Minerals. 1989, New York: Oxford University Press.

74. Cuillity, B.D., Elements of X-ray Diffraction; 2nd Edition. 2nd Edition ed. 1978,

Menlo Park: Addison-Wesley Publishing Company.

75. Dean, J.R., Practical Inductively Coupled Plasma Spectrometry. 2005, Chichester:

John Wiley & Sons.

76. Day, R.W., Soil Testing Manual; Procedures, Classification Data & Sampling

Procedures. 2001, New York: McGraw-Hill.

77. Adams, M.J., Chemometrics in Analytical Spectroscopy. 1995, Cambridge: The

Royal Society of Chemistry.

78. Shane, P., Tephrachronology: A New Zealand Case Study. Earth Science

Reviews, 1999. 49: p. 223-259.

79. Sirius, Sirius 7.0 Software for Multivariate Analysis and Experimental Design; User

Guide. 1998, Bergen: Pattern Recognition Systems AS.

80. E. Lewis, J.S., E. Lee and L. Kidder, Near-Infrared Chemical Imaging as a

Process Analytical Tool, in Process Analytical Technology, K. Bakeev, Editor.

2005, Blackwell Publishing: Oxford. p. pp. 187–225.

81. Geladi, J.B.a.P., Hyperspectral NIR image regression part II: dataset

preprocessing diagnostics. Journal of Chemometrics, 2006. 20: p. pp. 106–119.

82. Abollino, O., et al., Heavy Metals in Agricultural Soils from Piedmont, Italy.

Distribution, Speciation and Chemometric Data Treatment. Chemosphere, 2002.

49: p. 545-557.

83. Gardiner, W.P., Statistical Analysis Methods for Chemists. 1997, Cambridge: The

Royal Society of Chemists.

84. Bezdek, J.C., Pattern Recognition with Fuzzy Objective Function Algorithms.

1982, New York: Plenum Press.

85. Massart, D.L., et al., Chemometrics; A Textbook. 1988, New York: Elsevier

Science Publishers.

86. Kokot, S., G. King, and D.L. Massart, Application for Chemometrics for the

Selection of Microwave Digestion Procedures. Analytica Chimica Acta, 1992. 268:

p. 81-94.

87. Meglen, R.R., Chemometrics; Its Role in Chemistry & Measurement Sciences.

Chemometrics & Intelligent Laboratory Systems, 1988. 3: p. 17-29.

88. Kokot, S. and G. Ayoko, Chemometrics & Statistics; Multicriteria Decision Making,

in Encyclopedia of Analytical Sciences, P.J. Worsfold, A. Townshend, and C.F.

Poole, Editors. 2005, Elsevier: Oxford.

89. DecisionLab, Getting Started Guide. 2000, Monteal: Visual Decision Inc.

90. Keller, H.R., D.L. Massart, and J.P. Brans, Multicriteria Decision Making: A Case

Study. Chemometrics & Intelligent Laboratory Systems, 1991(11): p. 175-189.

3.0 RESULTS & DISCUSSION: CHARACTERISATION OF

SOILS

This chapter considers the results acquired through XRD and ICP analysis as well

as the results obtained from the NIR analysis of soils. Information relating to the CRM

analysis and calibration plots for ICP analysis are located in the appendix (Tables 7.4 –

4.17 & Figures 7.18-7.29).

3.1 X-Ray Diffraction Analysis

XRD analysis was employed to define the mineralogy of each soil, to identify and

quantify the dominant clay minerals present as well as to determine the amorphous

content in each sample. A summary of the minerals identified and quantified by XRD

analysis is presented in Table 3.1. The minerals identified by this method were Quartz,

Kaolinite, Anatase, Albite, Goethite, Microcline, Muscovite, Chlorite, Orthoclase, and

Montmorillonite.

The main constituent by far for all of the soils analysed was quartz, varying from

22.4 to 96.4 wt. %. Kaolinite was also present in the majority of soils (ranging from ND,

not detected, to 43.3 wt. %) along with Anatase and Albite. Goethite, Microcline,

Muscovite, Chlorite, Orthoclase and Montmorillonite were detected at low levels in some

soils.

By comparing the analytical results obtained from the air-dried slides with the

glycolated prepared slides it was possible to identify the soils containing expandable

clays such as smectite, on the basis of the d-spacing shift after glycolation. Thus, the

soils which were found to contain an illite/smectite mixed layer according to the thin film

preparation method (Chapter 2.2.1) were 4870-7, 4870-14, 4870-15, 4870-19, and 4870-

21 (Green, Table 3.1). Analysis of the diffraction patterns produced by the air-dried and

glycolated slides allowed identification of the minerals present but prevented

quantification of these minerals.

Corundum was employed as an internal standard in order to quantitate the

minerals using the powder preparation method (Chapter 2.2.2). The computer program,

SIROQUANT 2.5®, enabled the mineralogical composition of each sample to be

Tab

le 3

.1 –

Sum

mar

y of

XR

D R

esul

ts.

Sa

mp

le

Nu

mb

er

Sit

e

Nu

mb

er

Ho

rizo

n

Qu

art

z

%

Ka

oli

n-

ite

%

An

at-

a

se

%

Alb

ite

%

G

oe

- th

ite

%

Mic

ro-

cli

ne

%

Mu

sc

o-

vit

e%

C

hlo

r-

ite

%

Ort

ho

- c

las

e%

Il

lite

%

Il

lite

/ S

me

ctI

te%

A

mo

r-

ph

ou

s %

T

ota

l %

4870

-1

1 B

2 70

.6

14.4

0.

5 N

D

ND

N

D

ND

0.

2 N

D

ND

N

D

14.3

10

0.0

4870

-2

2 A

89

.9

2.2

0.4

ND

N

D

3.5

2.6

ND

N

D

ND

N

D

1.3

99.9

4870

-3

4 B

2 46

.0

27.1

0.

6 1.

9 4.

4 N

D

ND

0.

2 N

D

ND

N

D

19.9

10

0.1

4870

-4

5 A

77

.5

4.2

0.6

ND

N

D

3.3

0.5

ND

N

D

ND

N

D

14.0

10

0.1

4870

-5

7 B

1 81

.3

6.9

0.4

0.9

ND

9.

6 0.

6 N

D

ND

N

D

ND

0.

2 99

.9

4870

-6

8 A

76

.4

ND

N

D

8.8

ND

N

D

ND

N

D

7.0

ND

N

D

7.8

100.

0

4870

-7

9 A

78

.3

ND

0.

4 4.

7 N

D

ND

N

D

ND

3.

7 N

D

2.5

10.5

10

0.1

4870

-8

11

B1

86.7

3.

0 0.

6 1.

6 1.

6 3.

6 0.

4 N

D

ND

N

D

ND

2.

5 10

0.0

4870

-9

12

A

96.4

N

D

0.4

1.1

ND

N

D

ND

N

D

1.6

ND

N

D

0.6

100.

1

4870

-10

14

B1

76.8

6.

8 0.

4 N

D

ND

1.

8 N

D

ND

N

D

ND

N

D

14.2

10

0.0

4870

-11

15

A

81.3

5.

3 0.

3 N

D

ND

N

D

ND

0.

3 N

D

ND

N

D

12.8

10

0.0

4870

-12

18

A

73.9

6.

1 0.

4 N

D

ND

N

D

ND

N

D

ND

N

D

ND

19

.7

100.

1

4870

-13

19

A

57.0

6.

2 0.

4 0.

1 4.

5 1.

0 N

D

1.8

ND

0.

7 N

D

28.2

99

.9

4870

-14

20

A

51.7

20

.9

0.5

ND

0.

7 3.

4 N

D

0.3

ND

2.

9 2.

0 17

.6

100.

0

4870

-15

26

B2

24.0

43

.3

1.1

ND

11

.0

ND

N

D

1.1

ND

N

D

18.1

1.

3 99

.9

4870

-16

30

B1

48.7

24

.0

0.7

1.0

2.4

2.1

ND

1.

5 N

D

ND

N

D

19.6

10

0.0

4870

-17

31

B1

53.6

17

.1

0.8

0.6

ND

2.

3 N

D

ND

N

D

ND

N

D

25.6

10

0.0

4870

-18

33

B2

67.3

10

.5

0.3

ND

2.

6 N

D

ND

N

D

ND

N

D

ND

19

.3

100.

0

4870

-19

36

B2

22.4

38

.4

1.1

0.5

5.8

ND

N

D

ND

N

D

ND

18

.0

13.9

10

0.1

4870

-20

38

B1

73.5

1.

3 N

D

11.9

N

D

ND

N

D

ND

7.

0 N

D

ND

6.

3 10

0.0

4870

-21

40

B2

37.5

13

.1

0.5

7.3

ND

N

D

10.7

N

D

ND

N

D

6.7

24.3

10

0.1

4870

-22

42

B2

82.8

3.

3 N

D

4.3

ND

N

D

ND

N

D

4.2

ND

N

D

5.4

100.

0

4870

-23

43

B1

27.0

40

.1

0.7

ND

2.

3 N

D

ND

0.

4 N

D

ND

N

D

29.6

10

0.1

4870

-24

47

B2

66.0

9.

1 0.

9 0.

4 1.

3 1.

4 N

D

0.4

ND

N

D

ND

20

.6

100.

1

4870

-25

48

B1

44.7

38

.9

1.0

ND

2.

0 N

D

ND

N

D

ND

N

D

ND

11

.7

99.9

4870

-26

Bac

kyar

d 50

.8

8.7

0.5

8.8

1.4

ND

6.

5 N

D

ND

N

D

ND

23

.3

100.

0

4870

-27

Mel

cann

® S

and

71.1

0.

9 N

D

7.1

ND

N

D

ND

N

D

3.7

ND

N

D

17.3

10

0.1

ND

indi

cate

s no

t det

ecte

d.

calculated from the recorded X-ray diffraction pattern. This software employs a

calculation method developed by H.M.Rietveld in the late nineteen sixties. To be able to

quantify the mineral composition using this method, it is, first necessary to know the

crystal structure of the minerals present in the sample. SIROQUANT 2.5® calculates a

synthetic X-ray spectrum for each mineral and then the program compares the synthetic

spectrum with the measured spectrum. This initial comparison usually shows some

deviation. It is therefore necessary for the user to refine the synthetic spectrum, by

modifying the base line, peak width and height to obtain a satisfactory fit between the two

spectra.

In any method involving powder preparation, inaccurate sampling and

inhomogeneous mixing can present problems. These processes, where error can

potentially be introduced, are not trivial and can lead to significant error in the final result.

Such matters are discussed by Klug and Alexander 86. The error associated with the

recorded XRD results (Table 3.1) is estimated to be approximately +10% based on

knowledge acquired from previous soil analysis and validation using these methods.

The XRD results were used to select soil samples for the NIR spectroscopic

studies discussed in the following chapters.

3.2 Inductively Coupled Plasma Analysis

The soils were analysed using ICP methods as a confirmatory technique to

evaluate the XRD results. The ICP characterisation analysis was successful and the

elemental compositions determined. A results summary table is presented in Table 3.2.

The major elements, measured as oxides using this method, were Aluminium, Calcium,

Iron, Manganese, Silicon and Titanium. The minor elements, also measured as oxides,

were Potassium, Barium, Magnesium, Sodium, Strontium and Phosphorus. The ICP

results reflected the complementary XRD results (Chapter 3.1) with silicon proving to be

the major constituent for all of the soils, ranging in concentration from 19.4 to 91.6 wt. %.

The amount of Manganese, Barium, Strontium and Phosphorous detected in all samples

was minimal. The calculated Total Weight Percent was approximately 100 for each of the

soils with the exception of 4870-1, 4870-2, 4870-5, 4870-10 and 4870-11 (Orange, Table

3.2).

Tab

le 3

.2 –

Sum

mar

y of

ICP

Res

ults

.

M

ajo

r O

xid

es

M

ino

r O

xid

es

Sa

mp

le

Nu

mb

er

Sit

e

Nu

mb

er

Ho

rizo

n

Al 2

O3

wt.

%

Ca

O

wt.

%

Fe

2O

3

wt.

%

Mn

O

wt.

%

SiO

2

wt.

%

TiO

2

wt.

%

K2O

w

t. %

B

a

mg

/L

Mg

O

wt.

%

Na

2O

w

t. %

S

r m

g/L

P

2O

5

wt.

%

LO

I %

T

ota

l w

t. %

4870

-1

1 B

2 8.

15

0.02

1.

83

0.00

2 26

.6

0.69

2 0.

37

76.9

0.

14

0.01

7.

55

0.01

40

4.52

42

.4

4870

-2

2 A

3.

42

0.02

0.

98

0.00

4 22

.3

0.66

4 0.

34

142

0.10

0.

03

14.6

0.

0083

2.

26

30.1

4870

-3

4 B

2 13

.5

0.02

6.

68

0.00

4 66

.8

0.73

0 0.

19

75.8

0.

21

<LL

D

14.6

0.

0588

8.

16

96.3

4870

-4

5 A

3.

74

0.01

1.

79

0.01

3 87

.3x

0.98

7 0.

08

42.7

0.

05

<LL

D

4.21

0.

0282

3.

18

97.2

4870

-5

7 B

1 2.

73

0.04

1.

21

0.00

6 19

.4

0.54

3 0.

13

66.6

0.

07

0.11

12

.3

0.01

36

3.75

28

.0

4870

-6

8 A

6.

06

0.23

1.

14

0.05

9 84

.0x

0.67

3 1.

25

430

0.14

1.

46

94.3

0.

0306

2.

73

97.8

4870

-7

9 A

3.

18

0.08

0.

99

0.01

8 86

.8x

0.54

6 0.

66

226

0.09

0.

65

37.2

0.

0470

4.

30

97.4

4870

-8

11

B1

3.43

<

LLD

2.

38

0.00

3 86

.9x

1.06

0.

38

127

0.04

0.

19

21.1

0.

0644

3.

37

97.8

4870

-9

12

A

0.52

0.

01

0.10

0.

002

91.6

x 0.

552

0.01

38

.8

0.01

<

LLD

6.

88

0.01

07

2.21

95

.0

4870

-10

14

B1

5.39

0.

01

1.95

0.

003

39.9

0.

801

0.32

10

3 0.

07

<LL

D

19.1

0.

0185

3.

57

52.0

4870

-11

15

A

3.63

0.

03

0.84

0.

005

19.2

0.

660

0.06

39

.0

0.06

<

LLD

13

.7

0.02

30

3.53

28

.0

4870

-11

dup

15

A

3.52

<

LLD

0.

79

0.00

5 90

.7x

0.62

7 0.

08

32.0

0.

04

<LL

D

12.6

0.

0312

3.

53

99.3

4870

-12

18

A

4.37

0.

06

0.88

0.

003

86.0

x 0.

625

0.13

48

.5

0.08

<

LLD

8.

40

0.01

92

5.67

97

.9

4870

-13

19

A

9.87

<

LLD

8.

75

0.00

4 76

.5

0.58

8 0.

59

85.9

0.

16

0.01

8.

96

0.03

19

5.24

10

1.7

4870

-14

20

A

13.2

<

LLD

2.

41

0.00

3 77

.0

0.60

3 0.

94

105

0.25

0.

02

22.0

0.

0179

5.

55

99.9

4870

-15

26

B2

26.5

x <

LLD

7.

32

0.00

3 53

.2

1.04

0.

53

95.7

0.

31

0.02

14

.5

0.04

18

11.0

10

0

4870

-16

30

B1

15.9

<

LLD

4.

29

0.00

2 68

.7

0.45

9 0.

64

54.4

0.

29

<LL

D

11.2

0.

0190

7.

25

97.6

4870

-17

31

B1

11.0

<

LLD

3.

07

0.00

6 77

.4

1.19

0.

34

176

0.35

0.

11

32.0

0.

0268

6.

14

99.6

4870

-18

33

B2

6.00

<

LLD

4.

61

0.01

3 84

.8x

0.23

4 0.

24

48.2

0.

10

<LL

D

7.34

0.

0191

3.

19

99.2

4870

-19

36

B2

23.0

x <

LLD

9.

61

0.00

8 53

.8

1.23

0.

62

141

0.47

0.

09

9.70

0.

0347

11

.5

100

4870

-20

38

B1

5.11

0.

21

1.53

0.

008

84.7

x 0.

244

1.00

35

9 0.

22

1.35

78

.1

0.04

89

1.83

96

.3

4870

-21

40

B2

13.3

0.

01

4.34

0.

016

57.9

0.

420

1.50

27

60x

0.91

0.

69

109

0.03

09

9.83

89

.2

Tab

le 3

.2 (

co

nti

nu

ed

) – S

umm

ary

of IC

P R

esul

ts.

4870

-22

42

B2

3.63

0.

03

1.64

0.

016

90.2

x 0.

233

1.28

28

5 0.

14

0.50

28

.5

0.02

03

1.80

99

.5

4870

-23

43

B1

23.7

x <

LLD

6.

31

0.00

3 56

.8

0.72

3 1.

08

152

0.39

0.

03

14.9

0.

0302

10

.8

99.8

4870

-24

47

B2

6.68

0.

01

3.96

0.

069

69.9

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4870-4 4870-10 4870-15 4870-20

Figure 3.1 – Raw NIR spectra collected according to Chapter 2.4

over the spectral range 7500 - 4000cm-1.

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On completing the ICP sample preparation, it was observed that soils 4870-1,

4870-2, 4870-5, 4870-10 and 4870-11 contained a very fine white precipitate which could

not be removed with the addition of Hydrogen Peroxide. It was also observed that these

five soils recorded significantly low Total Weight Percent values. The sample preparation

(Chapter 2.3.1) required the samples to be dissolved using 1:3 AR nitric acid with heating

and stirring.

The literature revealed that the solubility of silica is affected by the pH and

temperature of solution 87-89 with SiO2 becoming less soluble as the concentration of nitric

acid increases. The literature also revealed that SiO2 becomes less soluble as the

temperature of the solution decreases. It was therefore proposed that the combination of

the concentrated nitric acid and high temperatures has lead to the precipitation of Silica

on cooling these five samples and hence, the weight percent results for SiO2 were

underestimated. To test this theory, a duplicate was prepared, 4870-11 dup, in order to

determine what effect the precipitation had on the overall ICP results. The duplicate was

successfully prepared with no precipitate and an obvious discrepancy in the amount of

SiO2 was observed (19.18 wt. % and 90.70 wt. %) while the remaining elements analysed

displayed comparable results.

The repeat analysis of the five samples (4870-1, 4870-2, 4870-5, 4870-10, 4870-

11) was deemed unnecessary as the ICP and XRD results are complementary for the

purposes of this study. The XRD results already obtained could sufficiently explain the

silica content of these soils for the purposes of this study and hence, repeat analysis

would not reveal any information which was not already available from the XRD results.

Therefore, there was no advantage offered by repeating the analysis.

3.3 Initial NIR Analysis

3.3.1 RAW SPECTRA AND OBSERVATIONS

Duplicate NIR spectra were recorded from all 25 soils pre-treated according to

Chapter 2.1 and analysed according to Chapter 2.4. A collection of representative

spectra are displayed in Figure 3.1. The spectra were recorded over the range 10000 –

4000cm-1. No significant bands were evident above 7500cm-1 and hence this region was

not utilised in this study. The recorded spectra from all soils displayed a peak at

approximately 7070cm-1 of varying intensities with a broad shoulder peak slightly higher

Table 3.3 – NIR Absorption assignments as compared with literature values

Functional Group

Observed (cm-1)

Literature 3, 90-92 (cm-1)

Assignment Origin

C-H2 4049 C-H combination C-H2 4252 C-H2 bending combination C-H

Weak absorptions ~4050-4400 4283 C-H/C-C combination

Mineral/Organic Matter

O-H Characteristic

doublet ~4545

4545 Al-OH bending/O-H stretching combination Kaolinite

O-H Broad peak ~5260 5200 H-O-H bending/O-H

stretching combination Adsorbed H2O

C-H2 5666 C-H2 5797 C-H3

Weak absorptions ~5660-5880 5880

C-H stretching 1st overtone

Mineral/Organic Matter

O-H Weak

absorptions ~7100

7100 H2O 2nd Overtone Molecular Bound H2O

O-H Characteristic

doublet ~7140

7143 O-H stretching 1st overtone Kaolinite

C-H2 7169

CH3

Weak absorptions ~7170-7350 7353

C-Hn 1st overtone

combination Mineral/Organic

Matter

at 7200cm-1. Furthermore, a sharp peak with a broad shoulder at a slightly higher

wavenumber was also observed with varying intensities in all recorded spectra at

approximately 4530cm-1. A prominent peak at approximately 5220cm-1 of varying

intensities was observed in all spectra. Minor differences in all spectra are visible in the

C-H combination region between 4500cm-1 and 4000cm-1.

The recorded spectra share similarities with spectra found in the literature 3, 90, 91.

The literature and recorded spectra display absorptions primarily due to overtone and

combination bands of C-H, O-H, R-OH and C=O groups. Majority of the recorded spectra

show strong diagnostic absorption bands related to vibrational processes involving

hydroxyl units. The absorptions are of considerable complexity due to broad and

overlapping bands.

The raw recorded spectra (Figure 3.1) exhibit many similar characteristic

absorptions as well as overlapping peaks making it difficult to interpret the mass of

chemical information contained in each individual spectrum. Chemometrics methods

offer data reduction and exploration beyond what can be established through visual

interpretation of the spectra. Hence, it will later become necessary to employ

Chemometric data analysis methods (Chapter 4) to highlight and explain these spectral

differences.

3.3.2 PEAK ASSIGNMENTS

The characteristic absorption features of the soil NIR spectra are shown in Figure

3.1. The most obvious peak is a broad absorption at ~5200cm-1. Bands in this region are

typically associated with O-H combinations such as those that occur in H2O 92. Another

significant peak exists at approximately 7070cm-1. This peak is relatively sharp with a

broad shoulder at a slightly higher wavenumber. Together these two bands form a

characteristic doublet at ~7140cm-1. Absorptions that occur in this region are typically

attributed to the first overtone of the O-H stretch 90. A similarly shaped characteristic

doublet peak also exists at the opposite end of the spectra, at approximately 4545cm-1.

Absorption in this region is associated with combination bands involving an Al-OH

bending with an O-H stretching 90. Relatively weak absorptions occur between 4050-

4400, 5660-5880 and 7170-7350cm-1. Absorptions in these regions are attributed to

various C-Hn combination and overtone bands 91, 92. Table 3.3 is a summary of these

absorption assignments and compares them with the literature values 3, 90-92.

Wavenumber (cm-1)

a.) Quartz b.) Kaolinite c.) Anatase d.) Albite e.) Microcline f.) Goethite

Figure 3.2 – NIR spectra recorded from minerals a.) Quartz, b.) Kaolinite,

c.) Anatase, d.) Albite, e.) Microcline and f.) Goethite.

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After assigning these absorptions, it was thought that raw NIR spectra of the key

minerals (detected by XRD, Chapter 3.1) would support the above assignments. Figure

3.2 displays the recorded spectra of six significant minerals; quartz, kaolinite, anatase,

albite, goethite and microcline for comparison with the spectra collected from the soil

samples.

3.3.3 COMPARISON OF SOIL SPECTRA WITH MINERAL SPECTRA

Quartz, SiO2, is the most common mineral found on the surface of the earth 93. An

ideal quartz crystal is rhombohedral consisting of a six-sided prism terminating with six-

sided pyramids at each end 94. Framework silicates such as quartz (Fig 3.2a) do not

have prominent absorption features in the NIR region 90. Their intense fundamental

vibrations occur in the mid-infrared region around 1000cm-1 90. Small absorption bands

found near 7140 and 5260cm-1 are often visible due to the vibrational combinations and

overtones of molecular water bound within the mineral.

Kaolinite is a common mineral formed by weathering or hydrothermal alteration of

aluminium silicates, particularly feldspar 95. Kaolinite, with the chemical formula

Al2Si2O5(OH)4, falls in the layered phyllosilicate category of clay minerals 94. The

dioctahedral structure of kaolinite consists of a Si2O5 sheet bonded to a gibbsite, Al(OH)3

sheet. The spectrum of kaolinite (Fig 3.2b) shows a doublet absorption, a sharp peak with

a broad shoulder peak at about 7140cm-1. This is attributed to the first overtone of the O-

H stretching 90. A second doublet peak can also be found at approximately 4545cm-1.

This is a combination band attributed to Al-OH bending plus an O-H stretching 90. These

two doublet absorptions are characteristic and often a key for identifying kaolinite.

Anatase is one of the four forms of titanium dioxide found in nature (the other

three being Brookite, Rutile and TiO2 II). Anatase has a tetragonal crystal system and is

usually derived from other titanium-bearing minerals 94. Conversely, albite has the

chemical formula NaAlSi3O8 with triclinic crystallography 95. There is limited literature on

the NIR spectral absorptions for either albite or anatase as their diagnostic absorptions

fall outside the NIR region. Considering the chemical formula for anatase, one would

expect to see no significant NIR absorptions in the recorded range. Spectra of anatase

and albite are displayed in Figure 3.2c and 3.2d respectively. Weak absorption occurs at

5235cm-1 which is the result of H-O-H bending combined with O-H stretching of adsorbed

water.

Figure 3.3 – Comparison of NIR spectra collected from soils 4870-2, green (low kaolinite

2.2%, high quartz 89.9%) and 4870-15, red (high kaolinite 43.3%, low quartz 24.0%).

Wavenumber (cm-1)

Microcline has the chemical formula KAlSi3O8 organised according to a triclinic

crystal system 94. It can be seen from Figure 3.2e that weak absorptions occur at

approximately 7100, 5200 and 4500cm-1. Absorption in these regions are due the to

combination bands involving an H-O-H bend with an O-H stretch consistent with those

observed earlier in the albite and anatase spectra due to adsorbed water and impurities.

The iron oxide, goethite, is a common mineral with the chemical formula FeO(OH)

and is typically formed under oxidising conditions as a weathering product of iron bearing

minerals 94. The NIR spectrum of goethite shows little absorption in the recorded region.

Strong absorption features for goethite occur beyond the NIR region into the visible

region at approx 22 000 and 11 000cm-1 (450 and 900nm) 90. The broad absorptions that

are present in Figure 3.2f are caused by impurities in the goethite sample not the goethite

itself.

The absorptions and assignments discussed for the NIR sample spectra reflect,

and are supported by, the ICP and XRD characterisation results. For example, if we

compare the spectra of two soils (Figure 3.3), one which is high in kaolinite, 4870-15

43.3% according to the XRD results, and one which has low levels of kaolinite, 4870-2

2.2%. Reiterating, that the characteristic kaolinite bands are seen as doublet peaks at

7070 and 4530cm-1, the soil, 4870-2, with low kaolinite levels displays much weaker

absorptions in these regions than the high kaolinite soil, 4870-15, which displays stronger

and sharper absorptions in the regions of interest.

Figure 3.4 shows 5 spectra, chosen because they represent the highest

concentration for one of the 6 discussed minerals (Quartz, Kaolinite, Anatase, Albite,

Microcline and Geothite). All spectra are presented on a common scale so as to make it

possible to compare peak intensities. The spectrum labelled 3.4b 4870-15 has the

highest measured concentration for both kaolinite and goethite and hence there are only

5 spectra displayed representing the 6 discussed minerals.

According to the XRD results, soil 4870-9 has the highest concentration of quartz

(96.4%) in comparison to the other soils (the lowest quartz content being measured at

22.4%). The spectrum recorded from this soil (Figure 3.4a) is very similar to the spectra

measured from the quartz sample (Figure 3.2a). The spectrum from the soil displays

small broad absorptions at 5224 and 4533cm-1 due to the vibrational combinations and

overtones of molecular water bound within the mineral. Note that a broad absorption at

4500cm-1 and the absence of absorption at 7070cm-1 indicates that the –OH is present as

Wavenumber (cm-1)

Figure 3.4 – NIR Spectra of soils a). 4870-9 (quartz 96.4%),

b). 4970-15 (kaolinite 43.3%, goethite 11.0%), c). 4870-19 (anatase 1.1%),

d). 4870-20 (albite 11.9%), and e). 4870-5 (microcline 9.6%).

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a). 4870-9 (Quartz 96.4%, Kaolinite not detected)

b). 4970-15 (Kaolinite

43.3%, Goethite 11.0%)

c). 4870-19 (Anatase

1.1%, Kaolinite 38.4%)

d). 4870-20 (Albite


e). 4870-5 (Microcline


molecular bound water 90. The absence of the characteristic kaolinite ‘doublets’ is

consistent with the XRD results which detected no kaolinite present in this particular soil.

The other minerals detected by the XRD analysis, anatase (0.4%), albite (1.1%) and

orthoclase (1.6%), seem to have had no visible effect on the spectra.

Soil 4870-15 has the highest measured kaolinite concentration (43.3%) as well as

the highest measured goethite concentration (11.0%), as recorded by the XRD results.

Therefore, it is not surprising that the recorded spectra for this soil (Figure 3.4b) is similar

to that recorded from the kaolinite (Figure 3.2b) in the regions 7000-7200cm-1 and 4500-

4650cm-1. Both spectra show strong doublet absorptions at ~7140 and 4545cm-1

characteristic of kaolinite. The absorptions present in the soil spectra are not as sharp or

refined as those present in the kaolinite spectrum. The broad peak at 5225cm-1 indicates

that there is also molecular bound water present in the soil sample. It seems the soil

spectrum has been scarcely affected by the goethite concentration (11.0%) since goethite

has little absorption in the recorded region.

There is little variation in the amount of anatase present in the analysed soils.

The concentration of anatase ranges from ‘not detected’ to 1.1%. Since minimal anatase

was detected it is unlikely that it will have much of an effect on the recorded spectra.

Figure 3.4c illustrates a spectrum recorded from soil 4870-19 with 1.1% anatase. This

soil was also recorded as having 38.4% kaolinite which explains the doublet absorptions

at 7140 and 4545cm-1. It seems that if there was any effect from the anatase on the

spectrum, it has been masked by these strong kaolinite absorptions. It is however

interesting to compare Figure 3.4b (43.3% kaolinite) with Figure 3.4c (38.4% kaolinite).

The relative intensities support the quantitative XRD measurement for the concentration

of kaolinite. i.e. 4870-15 has slightly more kaolinite present than 4870-19. Hence the

absorptions at 7140 and 4545cm-1 are slightly more intense for 4870-15 than for 4870-19.

Soil 4870-20 has the highest recoded albite concentration (11.9%). The NIR

spectrum obtained from this soil is displayed in Figure 3.4d. The spectrum is very similar

to that recorded from albite itself (Figure 3.2d). The quartz content of this soil is

reasonably high (73.5%) and the kaolinite content low (1.3%) therefore the recorded soil

spectrum has only those absorptions identifiably caused by albite or quartz (discussed

earlier).

The final mineral of interest is microcline. The soil with the highest microcline

content according to the XRD results is 4870-5 (9.6%) and is illustrated in Figure 3.4e.

Once again the spectrum recorded from the soil is similar to that recorded from the

4870-4 4870-10 4870-15 4870-20

Wavenumber (cm-1)

Figure 3.5 – Second derivative NIR spectra collected according to

Chapter 2.4 over the spectral range 7500 - 4000cm-1.

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microcline (Figure 3.2e) with few absorptions present over the range recorded. Slight

absorptions occur at 7067 and 4526cm-1 which suggests the soil has a small amount of

kaolinite present. This is confirmed by the XRD results which state the concentration for

kaolinite is 6.9%. Once again the absorption at 5224cm-1 indicates adsorbed water within

the soil.

Overall, it is apparent that the spectra collected from the minerals themselves are

evident in those soils which have significant quantities of those minerals. Hence, it

should be possible to predict from the NIR spectra of a soil, the minerals present within

that soil and their estimated concentration. Multivariate data analysis is the next

progression to further investigate this theory. The spectra collected from the soils will be

subjected to Chemometrics methods and Multivatiate Data Analysis in order to establish

whether the methods available are capable of distinguishing between unrelated soils and

matching those of similar composition (Chapter 4).

3.3.4 SECOND DERIVATIVE SPECTRA

It is well known that the use of derivative spectra can prove useful in solving

digressing baselines as well as reduction of noise and analytical errors 96. Second

derivatives of the raw spectra, recorded according to Chapter 2.4, are displayed in Figure

3.5 and are used throughout Chapter 4.0 for Chemometrics analysis. Figure 3.5

illustrates that the 2nd derivative pre-treatment has successfully removed the problems

associated with the baseline evident in the raw spectral data (Figure 3.1) due to the

numerous constituents present with large variation in particle size. This pre-treatment

removes differences due to both, baseline shifts and slope differences across the

spectrum as a whole.

The practical feature of any derivative is that the signal at any wavelength is still

proportional to the concentration, just like the original absorbance band. In contrast,

other pre-treatment methods require a common baseline between all spectra. Since a

common baseline was not appropriate for all spectra in this study other pre-treatment

options were deemed unsuitable and the 2nd derivative pre-treatment method was

determined to be most appropriate.

A second derivative function has three lobes for each original spectral peak; 2

smaller positive bands (relating to the points of inflection of the original peak) as well as

the main central band minima (the original curve maxima). This complexity can make

a.) 4870-2 b.) 4870-4 c.) 4870-12

a.) API #9 b.) KGa-1a c.) KGa-2

Wavenumber (cm-1)

Figure 3.6 – NIR spectra recorded from high quartz, low kaolinte soils.

a.) 4870-2 (89.9% quartz, 2.2% kaolinite) b.) 4870-4 (77.5% quartz, 4.2% kaolinite)

c.) 4870-12 (73.9% quartz, 6.1% kaolinite).

Figure 3.7 – NIR spectra recorded from kaolinite a.) API #9, b.) KGa-1a, and c.) KGa-2.

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a.) 100 % kaolinite

b.) 75% kaolinte 25% soil c.) 55% kaolinte 45% soil

d.) 25% kaolinte 75% soil e.) 100 % soil.

Wavenumber (cm-1)

Figure 3.8 – Quartz-Kaolinite Mixture: KGa-1a kaolinite mixed with Soil 4870-2

a.) 100 % kaolinite, b.) 75% kaolinte 25% soil, c.) 55% kaolinte 45% soil,

d.) 25% kaolinte 75% soil, e.) 100 % soil.

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derivatives difficult to interpret where there are multiple overlapping absorption bands for

two or more materials or functional groups creating the potential for asymmetrical band

profiles. Thus, it must be noted that full interpretation of derivative profiles should always

be completed in conjunction with visible comparison to the original raw spectra.

3.4 NIR Analysis: Quartz-Kaolinite Mixtures

From the previous discussion of results (Chapter 3.3.3) it appears that as the

relative amount of kaolinite decreased and hence the amount of quartz increased, the

characteristic kaolinite absorptions would also decrease, and vice versa. In order to test

this hypothesis, three high quartz soils were mixed with three kaolinite powders in varying

ratios according to the procedure outlined in Chapter 2.5.

The NIR spectra collected from the three high quartz, low kaolinite soils 4870-2,

4870-4 and 4870-12 (Figure 3.6) have similar spectra, with weak characteristic sharp

peaks and a broad shoulder at ~7170 and 4545cm-1. These soil spectra also displayed a

broad asymmetrical peak at 5224cm-1. The NIR spectra collected from the three kaolinite

powders API#9, KGa-1a and KGa-2 (Figure 3.7) have similar spectral profiles displaying

two strong, well defined characteristic peaks at ~7170 and 4545cm-1 as well as a weak

absorption at 5225cm-1. These absorptions have been identified earlier in Chapter 3.3.3.

The three high quartz soils (Figure 3.6) were mixed with the three kaolinite powders

(Figure 3.7) in varying ratios according to Chapter 2.5.

Figure 3.8 shows spectra collected from the various concentrations of soil 4870-2

with kaolinite KGa-1a. As expected the 100% kaolinite sample has the strongest

absorption at 7170 and 4545cm-1 while the 100% soil spectrum has minimal absorptions

throughout the entire spectral profile. The diagram illustrates a trend where the

characteristic 7170 and 4545cm-1 peak intensities decrease as the relative quartz to

kaolinite ratio increases. More specifically, as the amount of kaolinite in the mix

decreases, the characteristic kaolinite absorptions at 7170 and 4545cm-1 become less

pronounced. This trend was evident for all three kaolinites mixed with all three soils

therefore only one ratio mixture (4870-2 with KGa-1a, Figure 3.8) has been used as an

example for illustration purposes.

In summary, it was concluded from this analysis that as the kaolinite is essentially

diluted by the quartz (present within the soil) the spectral absorptions at ~7170 and

4545cm-1 diminished. This trend observation will be investigated more using

Chemometrics methods in Chapter 4.0.

a). Room Temp. b). 100oC c). 200oC d). 300oC e). 400oC f). 500oC g). 600oC

Figure 3.9 – Raw spectra recorded from soil 4870-6 at temperatures; a.) Room

temperature, 28oC, b.) 100oC, c.) 200oC, d.) 300oC, e.) 400oC, f.) 500oC, g.) 600oC.

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3.5 NIR Analysis: Temperature Dependent

After establishing the effect that quartz and kaolinite have on the NIR spectra, an

experiment was conducted to confirm the assignment of the various water absorptions,

discussed earlier in this chapter. This analysis was performed by investigating the effect

of temperature on consequent soil spectra. Six soils were heated according to Chapter

2.6 with spectra being recorded at; room temperature (approximately 25oC), 100oC, and

then in 100oC increments until 600oC. Such spectra from soil 4870-6 are shown in Figure

3.9 and are representative of the trends noted in the other five soils analysed; hence, only

one soil is represented diagrammatically.

The composition of soil 4870-6 according to the XRD results is high in quartz,

76.4%, with no kaolinite detected. Soil 4870-6 was selected as it has no characteristic

kaolinite peaks at ~7170 and 4545cm-1 (as these can often ‘mask’ the weaker water

absorptions also present in this region). According to Viscarra Rossel 90, a spectrum that

has a 7170cm-1 absorption band but no 5225cm-1 band indicates that Al–OH is present

(e.g. Kaolinite) with only a small amount of water because of the weak 5225cm-1

absorption relative to a large 7170cm-1 absorption. Conversely, a strong absorption band

at 5225 cm-1, relative to a weak absorption at 7170cm-1 indicates a significant presence of

water and lack of Al–OH. Therefore, the presence of a strong broad band at 5225cm-1

and a weak absorption at 7170cm-1 (as observed in Figure 3.9a Room Temperature

Spectrum) indicates significant adsorbed water is present with little or no Kaolinite. This

correlates with the XRD results for soil 4870-6 (kaolinite not detected).

An absorption band present at approximately 5225cm-1 was identified earlier as

corresponding to adsorbed water. If this is the case the peak at 5225cm-1 should reduce

dramatically initially in the heating program (up to ~200oC) and the other bands at ~7170

and 4545cm-1 should reduce in intensity at higher temperatures. Figure 3.9 illustrates the

results of such an experiment.

The results (Figure 3.9) demonstrate that the intensity of the 5225cm-1 peak

decreased significantly between room temperature (Figure 3.9a) and 200oC (Figure 3.9c).

The broad peak at 5225cm-1 was assigned (Chapter 3.3.2) as a combination band

resulting from adsorbed water (H-O-H bend with an O-H stretch). The decrease of

intensity observed for the absorption at 5225cm-1 confirms this peak is indeed caused by

adsorbed water as the majority of this water has evaporated, and hence, the peak

diminished, by temperatures up to ~200oC.

The other significant peaks at ~7170 and 4545cm-1 are also seen to decrease in

Figure 3.9. These peaks are not largely affected by the lower temperatures (up to 200oC)

but once above 200oC the peak intensities begin to decrease consistent with molecular

bound water being driven off at these higher temperatures. The peaks are still evident at

600oC suggesting some R–OH or molecular bound water remains. The observations

reported from this experiment support Viscarra Rossell’s assignment of these peaks 90

due to the vibrational combinations and overtones of adsorbed or molecular bound water

within the minerals.

Another interesting observation from this experiment was the variation of the

spectra between 5500-6000cm-1 and 4000-4400cm-1. The latter region typically displays

absorptions due to –CH, –CH2 and –CH3 combinations while the absorptions in the region

5500-6000cm-1 are attributed to –CH, –CH2 and –CH3 first overtones. The variation over

the range of temperatures is most likely caused by the combustion degradation of such

groups present due to the organic matter within the soil sample. It is not within the scope

of this study to investigate this observation any further.

Chapter Summary

• This chapter illustrated the detailed analyses of soils by XRD, ICP and NIR.

Specifically, it demonstrated a direct qualitative link between the three sets of

results revealing a promising outlook for application within the forensic science

discipline.

• The XRD results revealed qualitative and quantitative characterisation data

relating to the mineral composition of each of the soils employed in this study.

The main constituent by far in all soils was Quartz. Kaolinite, Goethite, Microcline,

Muscovite, Chlorite, Orthoclase and Montmorillonite were detected in some soils.

• The ICP results revealed qualitative and quantitative elemental analysis for each

of the soils employed in this study. The precipitation of SiO2 during the

preparation of soils 4870-1, 4870-2, 4870-5, 4870-10 and 4870-11 was explained

according to the literature. The ICP results were in agreement with the

complementary XRD results.

• The NIR spectra were recorded and the observed absorptions successfully

assigned according to the literature. Further confirmation of the peak

assignments was established by comparing the spectra collected from the various

soils with spectra collected from known minerals. The relative intensities of the

identified NIR peak absorptions were seen to reflect the quantitative XRD and ICP

characterisation results.

• The Quartz-Kaolinite investigation revealed that as the relative amount of kaolinite

decreased (and hence the quartz content increased) the intensity of the

characteristic kaolinite absorptions also decreased.

• The temperature dependent NIR analysis confirmed weak absorptions occurring

at ~7170 and 4545cm-1 (often masked by the much stronger, sharper kaolinite

absorptions) were due to molecular bound water and the absorption occurring at

5225cm-1 was due to adsorbed water.

References

91. Klugg, H.P. and L.E. Alexander, X-Ray Diffraction Procedures, 2nd Edition. 2nd

Edition ed. 1974, New York: Wiley.

92. Iler, R.K., The Chemistry of Silica; Solubility, Polymerization, Colloid and Surface

Properties, and Biochemistry. 1979, Toronto: John Wiley & Sons.

93. Greenwood, N.N. and A. Earnshaw, Chemistry of the Elements. 1984, Oxford:

Pergamon Press.

94. Porterfield, W.W., Inorganic Chemistry; A Unified Approach. 1984, California:

Addison-Wesley.

95. Viscarra Rossel, R.A.M., R.N. McBratney, A.B., Determining the Composition of

Mineral-Organic Mixes using UV-Vis-NIR Diffuse Refelctance Spectroscopy.

Geoderma, 2006. 137: p. 70-82.

96. Viscarra Rossel, R.A.W., D.J.J. McBratney, A.B. Janik, L.J. Skjemstad, J.O.,

Visible, Near Infrared, Mid Infrared or Combined Diffuse Reflectance

Spectroscopy for Simultaneous Assessment of various Soil Properties.

Geoderma, 2006. 131: p. 59-75.

97. Madari, B.E.R., J.B. Machado, P.L.O.A. Guimaraes, C.M. Torres, E. McCarty,

G.W., Mid- and Near- Infrared Spectroscopic Assessment of Soil Compositional

Parameters and Structural Indicies in Two Ferralsols. Geoderma, 2006. 136: p.

245-259.

98. Purcell, D.E., Role of Chemometrics for At-feld Application of NIR Spectroscopy to

Predict Sugarcane Clonal Performance. Chemometrics & Intelligent Laboratory

Systems, 2007. 87: p. 113-124.

99. Ralph, J.I. The Mindat Mineral and Gem Directory. 2007 [cited 2007 25/7/07];

Mineral Descriptions]. Available from: http://www.mindat.org/index.php.

100. Klein, C.H., Cornelius S., Manual of Mineralogy. 1999, New York: John Wiley &

Sons.

101. Perkins, D., Mineralogy. 2002, New Jersey: Prentice Hall.

102. Dou, Y., et al., Artificial neural network for simultaneous determination of two

components of compound paracetamol and diphenhydramine hydrochloride

powder on NIR spectroscopy. Analytica Chimica Acta, 2005. 528(1): p. 55-61.

-10.0

-5.0

0.0

5.0

10.0

15.0

20.0

-15.0 -10.0 -5.0 0.0 5.0 10.0 15.0 20.0 25.0 30.0

PC 1 (48.0%)

PC

2 (

8.9

%)

Figure 4.1 – PCA Scores plot of all soils treated according to Chapter 2.1

(excluding outlier 4870-25 duplicate B). Objects coloured according to the

horizon from which they originated.

Horizon B2

Horizon B1 Horizon A

4.0 RESULTS & DISCUSSION: CHEMOMETRICS &

MULTIVARIATE DATA ANALYSIS

This chapter further explores the results discussed in the previous chapter and

expands on the interpretation of these results using Chemometrics and Multivariate Data

Analysis methods. Such methods are beneficial in establishing correlations and trends

within the NIR data matrices. It is within this chapter that a comprehensive answer to the

initial primary aims of this project will be attained; Is NIR spectroscopy combined with

Chemometrics capable of distinguishing between soils? And, If so, on what foundations

is this distinction based?

4.1 Pre-treatment of Data Matrices

All chemometrics discussed in this chapter was performed on the spectral data

discussed in Chapter 3.0 over the spectral range 4000-7500cm-1. All spectra were

converted to 2nd derivative spectra (for reasons discussed in Chapter 3.3.4) and the data

point density of each spectrum was decreased by averaging two data points to reduce

the number of variables by half. The resulting data matrices were autoscaled (Chapter

2.8.1).

4.2 Raw Soils: Initial NIR investigation

4.2.1 PRINCIPAL COMPONENT ANALYSIS

The duplicate spectra collected from the 25 soils prepared according to Chapter

2.1 were subjected to PCA in order to display visually the matching and discrimination of

these soils. The resulting scores plot (Figure 4.1) displays all spectra with the exception

of 4870-25 Duplicate B. This object displayed a peak shift over the entire measured

spectral range due to an instrument error, and hence, was deemed to be an outlier. The

scores plot accounts for 56.9% of variance with the first PC accounting for 48.0%.

It was first thought that the soils may group together based upon their

classification according to the Australian Soil Classification System 24 (Chapter 2.1).

Visual inspection of the PCA scores plot failed to reveal any trends correlating with the

classification of the soils. Interestingly, the objects were found to show some separation

based on the horizon from which they originated. Figure 4.1 demonstrates that some

a).

-10.0

-5.0

0.0

5.0

10.0

15.0

20.0

-15.0 -10.0 -5.0 0.0 5.0 10.0 15.0 20.0 25.0 30.0

PC 1 (48.0%)

PC

2 (

8.9

%)

b).

-8.0

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

-15.0 -10.0 -5.0 0.0 5.0 10.0 15.0 20.0 25.0 30.0

PC 1 (52.4%)

PC

2 (

7.6

%)

Figure 4.2 – Fuzzy Cluster Analysis; PCA Scores Plot of soils prepared according to

Chapter 2.1 and analysed according to Chapter 2.4 (excluding 4870-25 Duplicate B).

a). all objects including those displaying fuzzy membership (black),

b). fuzzy objects removed, leaving only those objects constituting three ideal classes.

● Objects belong to Class 1; ■ Objects belonging to Class 2; ♦ Objects belonging to Class 3.

Class 1 Class 2

Class 3

Fuzzy Objects

Class 2

Class 1 Class 3

association between objects is evident based on the horizon from which the soil

originated.

In Figure 4.1, those soil objects with mostly negative PC 1 scores originate from

Horizon A (green), those with mostly positive PC 1 scores originate from Horizon B2 (red)

and those that tend to spread about the origin originate from Horizon B1 (blue). The

duplicate analysis of one particular soil that originated from Horizon B2 (red) contributes

to most of the positive variance on PC 2. The soils from Horizon A are clustered

somewhat closer together than the soils originating from Horizons B1 and B2.

To understand why the objects originating from Horizon A cluster more so than

those originating from either B1 or B2 it is necessary to understand how the Horizons are

assigned in the first place (Chapter 1.4). The distinction between two horizons within a

soil profile (Eg. Between Horizon A and Horizon B) is based on a major change in the soil

profile which is observable by the human eye (Eg. A significant change in

colour/texture/particle size). A distinction within a horizon (Eg. Between sub-horizon B1

and B2) is based on a minor change in the soil profile which is less distinctive and hence

leads to more subjectivity when assigning the sub-horizon. Assigning sub-horizons is

therefore somewhat subjective and as a result ambiguities may result. It is conceivable

that the subjectivity associated with assigning sub-horizons correlates with the spread of

objects originating from Horizon B1 (blue) and B2 (red) being spread intermittently across

PC1 (in Figure 4.1).

4.2.2 FUZZY CLUSTER (FC) ANALYSIS

The duplicate spectra collected from the 25 soils prepared according to Chapter

2.1 and analysed according to Chapter 2.4 were subjected to Fuzzy Cluster Analysis

(SIRIUS© 97) (excluding outlier 4870-25 duplicate B). In this work, three classes were

nominated and the p index was set at 2.5. Reiterating from Chapter 2.8.4, a soft p index

is applied here allowing separation of the objects into the designated three classes in a

manner that encourages multiple class membership. In a three class model, a

membership value greater than 0.333 (1/3) indicates membership within that cluster.

The resultant FC model (Figure 4.2) relied upon 10 PC’s (Chapter 2.8.2) to

explain 80% of data variance as established from the Scree Plot (Figure 4.3). The scree

plot displays two lines, the lower (Blue) line showing the proportion of variance explained

by each PC and the upper (Pink) line showing the cumulative variance explained by up to

10 PC’s.

0

10

20

30

40

50

60

70

80

0 1 2 3 4 5 6 7 8 9 10

Pinciple Components

Pro

po

rtio

n o

f V

ari

an

ce

Variance Cumulative Variance

Figure 4.3 – Scree plot displaying 10 PC’s and 80% of data variance.

Table 4.1 – Fuzzy Cluster membership values compared to the XRD results. (Black indicates fuzzy objects and Bold indicates membership in a class).

XRD Results Sample Name

Soil Sampling

Site

Soil Horizon

Membership Values for

Blue Cluster


Red Cluster


Green Cluster

Quartz (%)

Kaolinite (%)

0.514 0.266 0.220 4870-1 1 B2

0.503 0.265 0.232 70.6 14.4

0.385 0.112 0.503 4870-2 2 A

0.389 0.102 0.509 89.9 2.2

0.249 0.566 0.185 4870-3 4 B2

0.255 0.553 0.192 46.0 27.1

0.435 0.135 0.430 4870-4 5 A

0.425 0.164 0.411 77.5 4.2

0.303 0.108 0.589 4870-5 7 B1

0.301 0.149 0.550 81.3 6.9

0.263 0.076 0.661 4870-6 8 A

0.312 0.105 0.583 76.4 ND

0.314 0.143 0.543 4870-7 9 A

0.319 0.142 0.539 78.3 ND

0.298 0.138 0.564 4870-8 11 B1

0.289 0.139 0.572 86.7 3.0

0.244 0.084 0.673 4870-9 12 A

0.305 0.122 0.573 96.4 ND

0.585 0.128 0.287 4870-10 14 B1

0.606 0.136 0.259 76.8 6.8

0.280 0.158 0.562 4870-11 15 A

0.283 0.148 0.568 81.3 5.3

0.298 0.158 0.544 4870-12 18 A

0.293 0.178 0.529 73.9 6.1

0.574 0.165 0.261 4870-13 19 A

0.492 0.197 0.311 57.0 6.2

0.335 0.462 0.203 4870-14 20 A

0.342 0.449 0.209 51.7 20.9

0.255 0.542 0.203 4870-15 26 B2

0.255 0.542 0.202 24.0 43.3

0.193 0.686 0.121 4870-16 30 B1

0.203 0.669 0.128 48.7 24.0

0.475 0.201 0.324 4870-17 31 B1

0.467 0.220 0.312 53.6 17.1

0.538 0.200 0.262 4870-18 33 B2

0.500 0.218 0.282 67.3 10.5

0.221 0.623 0.157 4870-19 36 B2

0.243 0.579 0.177 22.4 38.4

0.311 0.127 0.561 4870-20 38 B1

0.311 0.126 0.563 73.5 1.3

0.370 0.296 0.334 4870-21 40 B2

0.376 0.291 0.333 37.5 13.1

0.289 0.160 0.551 4870-22 42 B2

0.277 0.149 0.574 82.8 3.3

0.206 0.663 0.131 4870-23 43 B1

0.226 0.630 0.144 27.0 40.1

0.245 0.134 0.621 4870-24 47 B2

0.221 0.111 0.668 66.0 9.1

0.292 0.491 0.217 4870-25 48 B1

Previously determined outlier 44.7 38.9

ND – not detected

From the 49 objects subjected to FC analysis, eight objects were deemed to be

fuzzy objects (Black, Figure 4.2a), that is, those objects which have membership in more

than one class. Table 4.1 displays membership values for this model. By removing the

fuzzy objects from the matrix (Figure 4.2b), those objects that remain constitute three

classes of ideal objects. Figure 4.2a explains 56.9% of data variance with PC 1

accounting for 48.0% while Figure 4.2b explains 60.0% of data variance with PC 1

accounting for 52.4%.

In Figure 4.2, the clusters have been termed Class 1, Class 2 and Class 3 and

coloured Blue, Red and Green respectively. Class 3 is situated negative on PC 1 and

Class 2 is situated positive on PC 1 with Class 1 being situated around the origin and

hence between the two aforementioned classes. Therefore Class 3 is largely separated

from Class two by PC 1 and Class 1 is located intermediate of the other two classes.

Now that the association between the three classes has been established, it is

necessary to use other characterisation data to determine why these classes are

apparent. To do this the XRD results are required. Table 4.1 presents the fuzzy

clustering membership values along with the XRD results for all 25 soils. The results

have been coloured according to the class they were assigned as a result of the FC

analysis.

Those soils with a high membership value for Class 3 (Green) display a high

quartz content (96.4 - 66.0%) and relatively low or not detected kaolinite content (not

detected - 9.1%) according to the XRD results. In contrast, those soils with a high

membership value for Class 2 (Red) display XRD results with a much lower quartz

content (22.4 - 48.7%) and a comparatively higher kaolinite content (24.0 - 43.3%). The

soils with high membership values for the intermediate cluster, Class 1 (blue), display

quartz and kaolinite contents intermediate of the two aforementioned clusters (53.6 - 76.8

and 6.2 - 17.1% respectively). Those objects determined to be fuzzy, coloured black

share characteristics of more than one class and hence do not distinctively belong to any

one class.

The FC analysis has therefore shown that the separation between the soils on PC

1 is greatly dependent upon their relative quartz and kaolinte contents more specifically

than the horizon from which the soils originated as previously thought.

It is important to note, the distinction between the soils based on the relative

quartz and kaolinite contents does not conflict with the earlier observation that the soils

could be distinguished based upon the horizon from which they originated. In fact both

observations are acceptable and justified. As discussed earlier, (Chapter 1.4) the

assignment of the various horizons is based upon observations of colour, texture, particle

size, presence of mottles and course fragments within the soil profile; all of which are

visual characteristics of the soil and hence are largely subjective. Nevertheless, these

visual characteristics are evident as a result of the chemical composition of the soil. For

example, illuviation (Chapter 1.3.2) causes the downward movement and accumulation of

fine materials. The accumulation of these fine materials generally leads to a higher

concentration of clay minerals, such as Kaolinite, in the B horizon compared to the A

horizon. Likewise, the larger coarser particles, such as quartz, accumulate in the upper A

horizon.

Since the assignment of horizons is not based upon specific measurable

variables, significant subjectivity exists when assigning the horizons, as discussed earlier

in this chapter. This subjectivity is evident in the PC scores plot (Figure 4.1) and explains

why the soils originating from Horizons B1 and B2 are somewhat indistinguishable and

why the soils originating from Horizon A overlap with Horizon B soils. Thus, it can be said

that the soils could be distinguished according to their relative quartz and kaolinte

contents, and to a lesser extent can be distinguished based upon the horizon from which

they originated.

4.2.3 LOADINGS PLOTS

The interpretability of PC loadings plots becomes difficult when using 2nd

derivative spectra. The 2nd derivative pre-treatment introduces positive and negative

fluctuations to the loadings plots (Chapter 3.3.4). This makes it difficult to establish which

variables contribute significantly to a specific PC and hence makes it difficult to extract

useful information from the loadings plots. Loadings plots were considered in this study

however they provided no beneficial information and as a result have not been included

in this thesis.

-15.0

-10.0

-5.0

0.0

5.0

10.0

15.0

-20.0 -10.0 0.0 10.0 20.0 30.0 40.0

PC 1 (51.8%)

PC

2 (

12

.6%

)

KGa-1a

API 9

KGa-2

Soil 4870-12

Soil 4870-4

Soil 4870-2

Figure 4.4 – PCA scores plot of all spectra collected from high quartz soils 4870-2,

4870-4 and 4870-12 mixed in varying ratios with three different kaolinite powders,

KGa-1a, KGa-2 and API9 according to Chapter 2.5.

■ Unmixed soil;

■ Unmixed kaolinte;

▲ Soil 4870-12 mixed with the various kaolinites;

♦ Soil 4870-4 mixed with the various kaolinites;

● Soil 4870-2 mixed with the various kaolinites.

4.3 Quartz-Kaolinite Mixtures

4.3.1 PRELIMINARY PRINCIPAL COMPONENT ANALYSIS

The observation that the soils can be distinguished on the basis of the relative

quartz and kaolinite content, established in Chapter 4.2, was investigated further by

methods outlined in Chapter 2.5. A total of 240 spectra were collected from three high

quartz soils mixed in varying ratios with three different kaolinite powders. The resulting

data matrix (pre-treated according to Chapter 2.4) consisting of 240 objects and 226

variables (cm-1) was subjected to PCA analysis. The resulting scores plot accounts for

64.4% of data variance and is displayed in Figure 4.4.

In Figure 4.4, the unmixed soils contribute to most of the negative variance on

both PC1 and PC 2 while the API kaolinite contributes to most of the positive variance of

PC 1. The three kaolinites are separated strongly by PC 2 with KGa-2 contributing to

much of the positive variance and API 9 contributing to much of the negative variance.

The soil and kaolinite mixtures are spread across PC 1 with the higher soil concentrations

being negative on PC 1 and the higher kaolinite concentrations being positive on PC 1.

On the whole, separation on PC 1 is based on the relative soil:kaolinite ratios within the

mixtures and separation on PC 2 is based on the type of kaolinite in the mixture (ie. KGa-

1a, KGa-2 or API 9).

A PC scores plot was generated using only one soil, 4870-12 (as an example),

and its various mixes with each of the three kaolinites. The scores plot, Figure 4.5,

explains 71.4% of data variance with the first PC accounting for 57.6%. The colours in

this plot do not indicate the soil type, as this remained constant (4870-12), but indicate

three different kaolinite powders that were mixed with soil 4870-12. The unmixed soil

(red square) contributes to most of the negative variance on both PC 1 and PC 2, and

hence is located in the lower left corner of the plot. The unmixed API 9 kaolinite

contributes to most of the positive variance on PC 1 while the unmixed KGa-2 kaolinite

contributes to most of the positive variance on PC 2. The soil and kaolinite mixtures are

spread across PC 1, as in Figure 4.4. The higher the kaolinite concentration the more

that object contributes positively to PC 1 and the higher the soil concentration the more

that object contributes negatively to PC 1.

- 10 .0

- 5.0

0 .0

5.0

10 .0

15.0

- 2 0 .0 - 15.0 - 10 .0 - 5.0 0 .0 5.0 10 .0 15.0 2 0 .0 2 5.0 3 0 .0 3 5.0

PC 1 (57.6%)

PC

2 (

13

.8%

)

Soil 4870-12

API 9

KGa-1a

KGa-2

Figure 4.5 – PCA scores plot of all spectra collected from high quartz soil, 4870-12,

mixed in varying ratios with three different kaolinite powders, KGa-1a, KGa-2 and API 9.

■ Unmixed soil 4870-12;

■ Unmixed kaolinte;

▲ Soil 4870-12 mixed with kaolinite KGa-2;

▲ Soil 4870-12 mixed with kaolinite KGa-1a;

▲ Soil 4870-2 mixed with kaolinite API 9.

In Figure 4.5, a linear correlation is observed between the unmixed 4870-12 soil and

kaolinite KGa-2 as well as kaolinite KGa-1a. However, the data is much more scattered

for unmixed soil 4870-12 and API 9 kaolinite. This was probably the result of inadvertent

mixing of the soil with the kaolinite.

Summarising Figures 4.4 and 4.5, it is evident that the separation on PC 1 is

largely caused by the relative ratio of soil and kaolinite within the mixture. It is also

observed that the separation on PC 2 is caused by the type of kaolinite present within the

mixture. It is therefore concluded that NIR spectroscopy combined with PCA is able to

distinguish soils based on the amount of kaolinite present within the soil. Furthermore, it

is capable of distinguishing between the various types of kaolinites present within a soil

and the relative amounts of these kaolinites. Table 2.2 (Chapter 2) revealed differences

between the kaolinites based on origin, crystallinity and supplier. This is an area where

possible future work could be focused.

4.3.2 PRINCIPAL COMPONENT ANALYSIS USING ONE SOIL WITH ONE

KAOLINITE

Figure 4.6 displays two PC scores plots. The first plot, Figure 4.6a displays soil

4870-4 mixed with kaolinite KGa-1a and the second plot, Figure 4.6b, displays soil 4870-

12 mixed with kaolinite KGa-2. The scores plot for soil 4870-4 (Figure 4.6a) explains a

total variance of 72.2% with PC 1 accounting for 62.4% while the scores plot for soil

4870-12 (Figure 4.6b) explains a total variance of 80.1%, PC 1 accounting for 65.8%. In

both plots, the unmixed soil and unmixed kaolinite contribute to much of the positive

variance on PC 2 with the unmixed kaolinite contributing to most of the positive variance

on PC 1 and the unmixed soil contributing to most of the negative variance on PC 1. In

addition, the mixture of approximately 50:50 soil:kaolinite contributes to most of the

negative variance on PC 2 and as a result both plots resemble a ‘V’ shape. This V

pattern was observed in the PC scores plots for all three soils mixed with each of the

three kaolinites and hence these plots have not been illustrated here.

a).

-6

-4

-2

0

2

4

6

8

10

12

-30 -25 -20 -15 -10 -5 0 5 10 15 20 25

PC1 (62.4%)

PC

2 (

9.8

%)

Soil 4870-4Kaolinite KGa-1a

90:10

Soil:Kaolinite

80:20

Soil:Kaolinite

70:30

Soil:Kaolinite

60:40

Soil:Kaolinite

50:50

Soil:Kaolinite

40:60

Soil:Kaolinite

30:70

Soil:Kaolinite

10:90

Soil:Kaolinite

20:80

Soil:Kaolinite

b).

-8.0

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

10.0

-20.0 -15.0 -10.0 -5.0 0.0 5.0 10.0 15.0 20.0 25.0

PC 1 (65.8%)

PC

2 (

14.3

%)

Soil 4870-12

Kaolinite KGa-2

55:45

Soil:Kaolinite

25:75

Soil:Kaolinite

40:60

Soil:Kaolinite

85:15

Soil:Kaolinite 70:30

Soil:Kaolinite

Figure 4.6 – PC Scores plot a). soil 4870-4 mixed with kaolinite KGa-1a, and

b). soil 4870-12 mixed with KGa-2.

- 4

- 2

0

2

4

6

8

- 2 5 - 2 0 - 15 - 10 - 5 0 5 10 15 2 0 2 5

PC1 (60.0%)

PC

2 (

9.5

%)

Soil 4870-4

90:10

Soil:Kaolinite

80:20

Soil:Kaolinite

70:30

Soil:Kaolinite60:40

Soil:Kaolinite

50:50

Soil:Kaolinite

40:60

Soil:Kaolinite

Kaolinite KGa-1

20:80

Soil:Kaolinite

10:90

Soil:Kaolinite

30:70

Soil:Kaolinite

Soil 4870-4

90:10

Soil:Kaolinite

80:20

Soil:Kaolinite70:30

Soil:Kaolinite

60:40

Soil:Kaolinite

50:50

Soil:Kaolinite

Kaolinite KGa-1

10:90

Soil:Kaolinite20:80

Soil:Kaolinite

30:70

Soil:Kaolinite

40:60

Soil:Kaolinite

Figure 4.7 – PC scores plot displaying the original and duplicate results from soil

4870-4 mixed with kaolinite KGa-1a.

Table 4.2 – PROMETHEE out-ranking flows and ranking for Soil 4870-4 mixed with Kaolinite KGa-1a.

(Red indicates unmixed Soil 4870-4, Black indicates unmixed kaolinite KGa-1a and Blue indicates mixtures of

soil with kaolinite.)

Φ+ Φ

- Φ Rank Φ+ Φ

- Φ Rank

Soil 4870-4 0.7983 0.0001 0.7982 1

60 : 40 Soil : Kaolinite 0.1098 0.1664 -0.0566 17

Soil 4870-4 0.7944 0.0003 0.7941 2

50 : 50 Soil : Kaolinite 0.0569 0.2293 -0.1725 18

Soil 4870-4 0.7936 0.0005 0.7931 3

50 : 50 Soil : Kaolinite 0.0551 0.2291 -0.1740 19

90 : 10 Soil : Kaolinite 0.4252 0.0693 0.3559 4

50 : 50

Soil : Kaolinite 0.0556 0.2311 -0.1756 20

90 : 10 Soil : Kaolinite 0.4226 0.0697 0.3530 5

20 : 80

Soil : Kaolinite 0.0549 0.2472 -0.1923 21

90 : 10 Soil : Kaolinite 0.4187 0.0701 0.3486 6

40 : 60

Soil : Kaolinite 0.0402 0.2344 -0.1942 22

Kaolinite KGa-1a 0.4144 0.3098 0.1046 7

20 : 80 Soil : Kaolinite 0.0547 0.2492 -0.1945 23

Kaolinite KGa-1a 0.4152 0.3116 0.1036 8

40 : 60 Soil : Kaolinite 0.0397 0.2366 -0.1969 24

80 : 20 Soil Kaolinite 0.2142 0.1137 0.1006 9

30 : 70

Soil : Kaolinite 0.0350 0.2361 -0.2011 25

Kaolinite KGa-1a 0.4075 0.3086 0.0989 10

40 : 60 Soil : Kaolinite 0.0394 0.2415 -0.2021 26

80 : 20 Soil Kaolinite 0.2100 0.1188 0.0911 11

30 : 70

Soil : Kaolinite 0.0342 0.2371 -0.2030 27

80 : 20 Soil Kaolinite 0.2100 0.1188 0.0911 11

20 : 80

Soil : Kaolinite 0.0456 0.2501 -0.2045 28

70 : 30 Soil Kaolinite 0.1591 0.1539 0.0052 12

30 : 70

Soil : Kaolinite 0.0333 0.2400 -0.2067 29

70 : 30 Soil Kaolinite 0.1589 0.1614 -0.0026 13

10 : 90

Soil Kaolinite 0.0457 0.2947 -0.2491 30

70 : 30 Soil Kaolinite 0.1586 0.1639 -0.0053 14

10 : 90

Soil Kaolinite 0.0416 0.2962 -0.2545 31

60 : 40 Soil : Kaolinite 0.1119 0.1634 -0.0515 15

10 : 90

Soil Kaolinite 0.0398 0.2946 -0.2548 32

60 : 40 Soil : Kaolinite 0.1104 0.1637 -0.0533 16

The methodology outlined in Chapter 2.5 for soil 4870-4 mixed with kaolinite KGa-

1a was repeated, beginning with the preparation of the mixtures through to collecting the

spectra. This duplicate analysis was completed to establish whether repeat analysis

would provide consistent results. Figure 4.7 displays a PC scores plot of the original

analysis (blue) combined with the results from the duplicate analysis (orange). The

characteristic V pattern of the duplicate analysis was consistent with the original analysis.

The various sections of the PC scores plots (Figures 4.6 and 4.7) are related to

the relative ratios of soil and kaolinite in the mixtures. The objects with positive scores on

PC 1 are dominated by kaolinite with the unmixed kaolinite having the highest score on

this PC. It can be observed that as the contribution of kaolinite decreases, so too does

the objects’ contribution to PC 1 until the ratio of kaolinite and soil is approximately equal.

As the concentration of soil rises above 50% the reverse is observed. The scores

become more negative as the contribution of soil increases. It is therefore reasonable to

conclude that the PC 1 axis distinguishes the objects based on the type of mineral

(quartz/kaolinite) while the PC 2 axis distinguishes the objects based on the amount of

mineral. With such an obvious pattern emerging in these plots, one begins to wonder

whether it is possible to rank and/or predict the soil samples based on the amount of

quartz or kaolinite present. The Multi-Criteria Decision Making (MCDM) methods,

PROMETHEE and GAIA are useful when ranking of objects is required.

4.3.3 PROMETHEE AND GAIA

PROMETHEE and GAIA is part of Multicriteria Decision Making methods known

as outranking methods based on the principle of pairwise comparison. Both positive and

negative preference flows can be used to rank actions (objects) according to the criterion

(variables) (Chapter 2.8.4). In principle, there are at least two ways to construct a data

matrix; i). use the original multivariate data matrix, or, ii). use the compressed data matrix

containing the PCA scores. The later method is used here and has the advantage that

the data matrix constructed from the PCA scores excludes the residuals. Three

PROMETHEE modelling requirements are: nomination of the ranking sense, ie. maximise

or minimise; selection of the preference function; and the weighting. In this case, these

were chosen based on previous analyses 98, 99. The Gaussian preference function,

P(a,b), was selected with the threshold set as the standard deviation, s, for each PC

criterion. The criteria weights were uniformly set to 1 and the unmixed Soil 4870-4 was

used to decide the maximise/minimise ranking sense.

a).

-15

-10

-5

0

5

10

-20 -15 -10 -5 0 5 10 15 20

PC1 (50.2%)

PC

2 (

20.0

%)

600oC

300oC

28oC

100oC

200oC

400oC

500oC

Soil 4870-25

b).

-15

-10

-5

0

5

10

-20 -15 -10 -5 0 5 10 15 20

PC1 (47.9%)

PC

2 (

22.3

%)

600oC

24oC

500oC

400oC

300oC

200oC

100oC

Soil 4870-18

Figure 4.8 – Temperature dependent analysis: PC Scores plot a). soil 4870-25, and

b). soil 4870-18, both heated from room temperature up to 600oC.

PROMETHEE rank modelling was carried out with the data matrix consisting of 33

spectral objects, and 2PC’s (consistent with the data matrix used to plot Figure 4.6a;

72.2% data variance). Specifically, the various mixtures of Soil 4870-4 with KGa-1a were

compared. The net outranking flow, Φ, ranged from -0.2548 to 0.7982. The expectation

was that the unmixed Soil 4870-4 would rank at one end of the scale with the unmixed

kaolinite, KGa-1a, ranking at the opposite end, such that the various mixtures lie between

based on the relative concentrations of kaolinte and soil. The outranking flows are

displayed in Table 4.2. The results were not as obvious expected, with the unmixed Soil

4870-4, 90:10 soil:kaolinite and unmixed kaolinte KGa-1a all being ranked less than 10.

However, if the unmixed Soil 4870-4 and unmixed Kaolinite KGa-1a are ignored in the

rankings (ie. the mixtures only remain) the objects rank conceivably (with the exception of

20:80 & 30:70) based on their relative ratios. The GAIA plot, reflected the PCA scores

plot and hence has not been included.

4.4 Temperature Dependent NIR Analysis

Earlier, Chapter 3 revealed that much of the spectral information obtained from

the soils related either to the quartz and kaolinite present within the soil, or the moisture

content of the soil. The earlier sections of this chapter have focused only on the

relationship between quartz and kaolinite. This section will investigate the role of

moisture and its effects on the NIR spectral information.

The spectra collected according to Chapter 2.6 and pre-treated according to

Chapter 2.4 were subjected to PCA analysis. Two PC scores plots (relating to two of the

soils, 4870-18 and 4870-25) are displayed in Figure 4.8. The pattern observed in these

two soils was also observed in the other four soils analysed according to Chapter 2.6 and

hence these plots have not been included here. Two plots are displayed to show

consistency in the scores plots between different soils exposed to the temperature

various temperatures.

The PC 1 versus PC 2 scores plot in Figure 4.8a explains a total variance of

70.2% with PC 1 accounting for 50.2%. Each object represents a NIR spectrum collected

from soil 4870-25 as the temperature was increased from room temperature (28oC) up to

600oC. Similarly, the PC scores plot in Figure 4.8b represents the NIR spectra of soil

4870-18 as it was heated from room temperature (24oC) up to 600oC. This scores plot

accounts for 70.2% of data variance with PC 1 explaining 47.9%. For both plots, the

objects with positive scores on PC 1 were recorded at temperatures up to 300oC with the

spectra recorded at the lowest temperature accounting for most of the positive variance

on this PC. As the temperature of the soil increased the more the variance contribution

decreased. This was evident right from the lowest temperature (room temperature)

through to the highest temperature (600oC). Consequently, the object relating to the

spectrum collected at the highest temperature (600oC) contributed to most of the negative

variance on PC 1 and PC 2 for both plots. Most of the positive variance on PC 2 was

attributed to the midpoint 300oC spectrum, such that an upside down ‘V’ pattern resulted.

Complete interpretation of the PC scores plots (Figure 4.8) is difficult without the

use of loadings plots (Chapter 4.2.3) to establish which variables contribute significantly

to the various PC’s. Earlier, in Chapter 3.5, the absorptions occurring at ~7170 and

4545cm-1 were identified as being associated with molecular bound water and the

absorptions occurring at 5225cm-1 associated with adsorbed water. It is hypothesised

that the separation on PC 1 is attributed to the loss of adsorbed water (i.e. the reduction

in the 5225cm-1 peak) and the separation on PC 2 is attributed to the loss of molecular

bound water (i.e. the reduction in the 7170 and 4545cm-1 peaks) at higher temperatures.

However, without loadings plots or comparative analytical information relating to the

moisture content of the soil at each of the recorded temperatures it is not possible to

confirm this.

The methodology in Chapter 2.6 endeavoured to confirm the assignment of the

water absorptions recorded in the NIR spectra. Since this has been achieved (Chapter

3.5), further analysis is beyond the scope of this research. However, it has revealed an

area where future work could be focused.

Chapter Summary

• The initial PCA scores plots revealed that the soils prepared according to Chapter

2.1 could be distinguished between based on information pertained in the NIR

spectra.

• The soils were able to be distinguished according to their relative quartz and

kaolinte contents, and to a lesser extent were also able to be distinguished based

on the horizon from which they originated.

• Fuzzy clustering methods revealed three clusters were evident based on the

amount of quartz and kaolinte. Those soils with high quartz and low kaolinite

were largely separated from those soils with high kaolinte and low quartz.

• The PCA analysis of the three soils mixed with the three kaolinites supported the

earlier conclusion that soils can be distinguished based on the relative amounts of

quartz and kaolinite present within the soil.

• In addition, the quartz kaolinite investigation also revealed that NIR and PCA

could distinguish between the different types of kaolinite, suggesting that the NIR

spectral region also contains information describing variation within kaolinite.

Some suggestions as to why this distinction was evident is that NIR may be

sensitive enough to reveal information relating to the crystallinity, density,

molecular bound moisture or impurities within the kaolinite itself. Further work is

required to explain this observation.

• The PCA analysis of the temperature dependent spectra lead to the development

of a hypothesis that PC 1 separates the spectral objects based on the loss of

adsorbed water while PC 2 separates the spectral objects based on the loss of

molecular bound water over the duration of the heating program.

References



104. SIRIUS, Pattern Recognition Systems AS. 1998: Bergen, Norway.

105. Purcell, D.E., et al., A Chemometrics Investigation of Sugarcane Plant Properties

Based on the Molecular Composition of Epicuticular Wax. Chemometrics &

Intelligent Laboratory Systems, 2005. 76: p. 135-147.

106. Ni, Y., et al., Multi-wavelength HPLC fingerprints from complex substances: an

exploratory chemometrics study of the Cassia seed example. Analytica Chimica

Acta, 2009.

5.0 RESULTS & DISCUSSION: APPLICATION OF NIR TO

FORENSIC SCENARIO

The final objective outlined in the Introduction (Chapter 1.1) was to “Simulate a

forensic scenario, as an exploratory investigation, to establish the capabilities of such a

method being applied in the ‘real world’ ”. Essentially, this chapter responds to this final

objective and brings together the overall aim of ‘combining NIR and Chemometrics to

discriminate or match soils in a forensic investigation’.

5.1 The Scenario

Chapter 2.7 explained the three shoes (Leather Shoe, Jogger and Walk Shoe)

were used to simulate a walking motion on soils 4870-26 ‘Backyard Soil’ and 4870-27

‘Melcann® Sand’. Contact between the objects caused surface exchange between the

sole of the shoe and the grounds surface. Two sampling methods (Dry Brushed Method

and Wet Sampled Oven Dried Method) were used to collect the soil adhering to the

shoes. Comparison samples were taken from the impression that remained on the

ground’s surface. Duplicate NIR spectra were recorded. Tables and diagrams, additional

to what appears in this chapter, illustrating the sampling sites for the various shoes and

sampling methods are included in the Appendix (Table 7.2 and 7.3, Figures 7.1-7.8, 7.10,

7.12, 7.14 and 7.16).

5.2 Pre-treatment of Data Matrices

All chemometrics discussed in this chapter was performed on the spectral data

obtained from Soils 4870-26 ‘Backyard Soil’ and 4870-27 ‘Melcann® Sand’ according to

Chapter 2.7 over the spectral range 4000-7500cm-1. All spectra were converted to 2nd

derivative spectra (for reasons discussed in Chapter 3.3.4) and the data point density of

each spectrum was halved by averaging two data points to give one. The resulting data

matrices were autoscaled (Chapter 2.8.1).

-8

-4

0

4

8

12

-20 -15 -10 -5 0 5 10 15 20PC 1 (42.3%)

PC

2 (

7.4

%)

Figure 5.1 – PCA scores plot of the complete data matrix including 174 objects

(87 duplicate spectra) and 226 variables collected according to methodology

outlined in Chapter 2.7.

4870-27 (Sand) Dry Brushed

4870-27 (Sand) Wet Sampled

4870-26 (Soil) Dry Brushed

4870-26 (Soil) Wet Sampled

5.3 Principal Component Analysis

5.3.1 COMPLETE DATA MATRIX: 3 SHOES, 2 SAMPLING METHODS AND 2 SOILS.

All spectra collected during the forensic simulation formed a 174 (spectral objects)

by 226 (wavenumber) data matrix that was submitted to PCA. Specifically, the spectra

reflected the simulation involving the three shoes (Walk Shoe, Jogger and Leather Shoe)

contacting both soils (4870-26 ‘Backyard Soil’ and 4870-27 ‘Melcann® Sand’) using both

sampling methods (Dry Brushed and Wet Sampled). The resulting PC 1 versus PC 2

scores plot (Figure 5.1) displays all of the 174 spectral objects collected (Chapter 2.7)

and accounts for 49.7% of data variance with PC 1 explaining 42.3%. This data display

demonstrates the discrimination of soil 4870-26 from 4870-27, with PC 1 separating

4870-27 (negative scores; dark blue/light blue) from 4870-26 (positive scores;

red/orange). Furthermore, this PCA scores plot was able to distinguish between the two

methods used to collect the samples. PC 2 separates the Wet Sampled spectral objects

(negative scores) from the Dry Brushed sampled spectra (positive scores).

The previous chapter revealed that the soils were able to be discriminated

according to their relative quartz and kaolinte contents. The XRD results (Table 3.1)

reveal that the quartz and kaolinite composition for Soil 4870-26 is 50.8% and 8.7%

respectively. Soil 4870-27 has a higher quartz content, 71.1% but lower kaolinite content,

0.9%. With this knowledge it is possible to conclude that Figure 5.1 separates 4870-26

from 4870-27 on PC 1 on the basis of the quartz and kaolinite contents. The soil higher

in quartz (4870-27) is located with negative scores on PC 1 and the soil higher in kaolinite

(4870-26) with positive scores on the same PC. Furthermore, PC 2 separates those

spectra collected using the Wet Sampling method from those sampled using the Dry

Brushed method. Conclusions from Chapter 4.4 suggest that the separation on PC 2 is

based largely on the moisture content of the samples. The Wet Sampled soils were dried

in an oven at 105oC for 2 hours while the Dry Brushed samples were simply stored in a

desiccator. The oven dried samples, with a lower moisture content, are located negative

on PC 2 while the samples stored in a desiccator are located positive on PC 2.

It is known that, compared to quartz, kaolinite is more inclined to absorb moisture

due to its ability to structurally expand its layers 100. In Figure 5.1, the separation of the

high kaolinite soil (4870-26) on PC 2 is measurably greater than the separation evident

for the high quartz soil (4870-27) on the same PC. This suggests that more moisture was

lost from the high kaolinite soil (4870-26) during the drying step compared to the high

quartz soil (4870-27).

a).

-15

-10

-5

0

5

10

15

-15 -10 -5 0 5 10 15 20 25 30

PC 1 (21.2%)

PC

2 (

16

.9%

)

b).

-12

-8

-4

0

4

8

12

-10 -5 0 5 10 15 20

PC 1 (14.3%)

PC

2 (

9.5

%)

Figure 5.2 – PCA scores plots revealing separation based on sampling method

a). 4870-26 Backyard Soil spectra (excluding outliers). b). 4870-27 Melcann® Sand

spectra. Objects coloured according to sampling method used.

4870-27 Wet Sampled

4870-27 Dry Brushed

4870-26 Dry Brushed

4870-26 Wet Sampled

Backyard Soil

Melcann® Sand

5.3.2 SEPARATION BASED ON SAMPLING METHOD

A subset data matrix including 4870-26 ‘Backyard Soil’ (ie. no 4870-27 Melcann®

Sand’), using both sampling methods and all three shoes, with 96 objects (duplicate

spectra from 48 soil samples) and 226 (wavenumber) variables was subjected to PCA.

The resulting PC 1 versus PC 2 scores plot, Figure 5.2a, displays only the Soil 4870-26

‘Backyard Soil’ data subset, excluding two outliers (one duplicate scan corresponding to

the Walk Shoe using the Wet Sampled method and one duplicate scan corresponding to

the Walk Shoe using the Dry Brushed method), which displayed a peak shift over the

entire measured spectral range due to an instrument error. Figure 5.2a explains 38.1%

of data variance with PC 1 accounting for 21.2%.

A subset data matrix including 4870-27 ‘Melcann® Sand’ (ie. no 4870-26 Backyard

Soil’), using both sampling methods and all three shoes, with 78 objects (duplicate

spectra from 39 sand samples) and 226 (wavenumber) variables was subjected to PCA.

The resulting PC 1 versus PC 2 scores plot, Figure 5.2b, displays only 4870-27

‘Melcann® Sand’ subset using both the Dry Brushed and Wet Sampled methods. Figure

5.2b explains 23.8% with PC 1 accounting for 14.3%. In both plots, Figure 5.2a and 5.2b,

the separation of the Wet Sampled spectra from the Dry Brushed sampled spectra is

distinctive based on PC 1.

5.3.3 SEPARATION BASED ON SHOE TYPE USING DRY BRUSHED METHOD

A subset data matrix consisting of 18 objects (duplicate spectra of 9 Melcann®

Sand samples) and 226 (wavenumber variables) was subjected to PCA. The resulting

scores plot, Figure 5.3a, displays only the spectra collected from 4870-27 Melcann® Sand

using the Dry Brushed sampling method. Figure 5.3a explains 34.8% of data variance

with PC 1 accounting for 20.1%. In this Figure, the objects have been coloured according

to the shoe that was used to contact the sand. The objects coloured green indicate the

Jogger was used to simulate a walking motion and samples were taken from the sand

adhering to the shoe as well as from the impression that remained in the sand. The

purple objects indicate that the Leather Shoe was used to create the impression while the

blue objects indicate the Walk Shoe. It is interesting to observe the separation between

the different shoe types. Those objects corresponding to the Walk Shoe have negative

scores on PC 1 and positive on PC 2, and those objects corresponding to the Jogger are

positive on PC 1 and negative on PC 2. The objects corresponding to the Leather Shoe

do not contribute strongly to PC 1 or PC 2.

a).

4870-27 Melcann Sand

-16

-12

-8

-4

0

4

8

12

-10 -5 0 5 10 15

PC 1 (20.1%)

PC

2 (

14

.7%

)

b).

4870-26 Backyard Soil

-12

-8

-4

0

4

8

12

-25 -20 -15 -10 -5 0 5 10 15 20 25

PC 1 (28.6%)

PC

2 (

9.9

%)

Figure 5.3 – PCA scores plots of Dry Brushed sampling method only, revealing

separation based on shoe type a). Spectra collected from 4870-27 Melcann® Sand

using the Dry Brushed method for the three shoe types. b). Spectra collected from

4870-26 Backyard Soil using the Dry Brushed method for the three shoe types.

Objects coloured according to shoe type.

Walk Shoe

Walk Shoe

Jogger

Jogger

Leather Shoe

Leather Shoe

a).

4870-27 Melcann Sand

-12

-8

-4

0

4

8

12

-15 -10 -5 0 5 10 15 20

PC 1 (19.8%)

PC

2 (

10.1

%)

b).

4870-26 Backyard Soil

-16

-12

-8

-4

0

4

8

12

16

-15 -10 -5 0 5 10 15 20

PC 1 (12.0%)

PC

2 (

8.3

%)

Figure 5.4 – PCA scores plots of Wet Sampling method only, revealing separation

based on shoe type a). Spectra collected from Melcann® Sand using the Wet Sampled

method for the three shoe types. b). Spectra collected from Backyard Soil using the

Wet Sampled method. Objects coloured according to shoe types.

Walk Shoe

Walk Shoe

Leather Shoe

Leather Shoe

Jogger

Jogger

A subset data matrix of 36 objects (duplicate spectra of 18 Backyard Soil

samples) and 226 (wavenumber variables) was subjected to PCA. The resulting PC 1

versus PC 2 scores plot, Figure 5.3b, displays only the spectra collected from the 4870-

26 Backyard Soil using the Dry Brushed sampling method. Figure 5.3b explains 38.5% of

data variance with PC 1 accounting for 28.6%. The objects are again coloured according

to the shoe used to make the impression. The separation trend based on shoe types is

once again evident, as previous, in Figure 5.3a. The objects corresponding to the Walk

Shoe are located positive on PC 2 while the objects corresponding to the Jogger and

Leather Shoe (while not separated as distinctively) are located mainly negative on PC 2.

Such separation between the different shoe types (as observed in Figure 5.3) was not

anticipated prior to this analysis.

5.3.4 SEPARATION BASED ON SHOE TYPE USING WET SAMPLED METHOD

The subset comprising 30 duplicate spectra collected from the 4870-27 Melcann®

Sand, sampled according to the Wet Sampling method had the dimensions 60 x 226.

PCA analysis resulted in the scores plot, Figure 5.4a, explaining 29.9% of data variance

with PC 1 accounting for 19.8%. A separate subset with dimensions 60 x 226 was

created that included the 4870-26 Backyard Soil also sampled according to the Wet

Sampling method. PCA analysis resulted in the scores plot, Figure 5.4b, explaining

20.3% of data variance with PC 1 accounting for 12.0%. The objects have again been

coloured according to the shoe that was in contact with the sand/soil. PC 2 clearly

separates the Walk Shoe (blue), with positive scores, from the Jogger (green), with

negative scores (Figure 5.4a). However, the Leather Shoe objects (Purple) appear to be

randomly scattered. Conversely, there appears to be no discrimination evident in the

PCA display (Figure 5.4b) between the various shoes using the Wet Sampling method.

5.3.5 DISCUSSION OF THE SEPARATION BASED ON SHOE TYPE

The Figures discussed in this chapter thus far indicate that PCA is not only

capable of distinguishing the Melcann® Sand from the Backyard Soil, and the Dry

Brushed from the Wet Sampled method, but they also suggest that separation exists

between the shoe types; Jogger, Leather or Walk Shoe. It is therefore proposed that the

shoe sole plays an important role in the transfer of sand/soil particles from the ground

surface to the shoe.

P

o

s

i

t

i

v

e

N

e

g

a

t

i

v

e

Neutral

Human Skin

Leather

Glass

Quartz/Silica

Wool

Cellulose/Paper

Amber

Hard Rubber

Brass/Silver/Gold

Synthetic Rubber

PVC

Teflon

CottonSteel

+++

- - -

+

-

P

o

s

i

t

i

v

e

N

e

g

a

t

i

v

e

Neutral

Human Skin

Leather

Glass

Quartz/Silica

Wool

Cellulose/Paper

Amber

Hard Rubber

Brass/Silver/Gold

Synthetic Rubber

PVC

Teflon

CottonSteel

+++

- - -

P

o

s

i

t

i

v

e

N

e

g

a

t

i

v

e

Neutral

Human Skin

Leather

Glass

Quartz/Silica

Wool

Cellulose/Paper

Amber

Hard Rubber

Brass/Silver/Gold

Synthetic Rubber

PVC

Teflon

CottonSteel

P

o

s

i

t

i

v

e

N

e

g

a

t

i

v

e

Neutral

Human Skin

Leather

Glass

Quartz/Silica

Wool

Cellulose/Paper

Amber

Hard Rubber

Brass/Silver/Gold

Synthetic Rubber

PVC

Teflon

CottonSteel

+++

- - -

+

-

Figure 5.5 – Triboelectric Series: materials ranked in order of their decreasing tendency to

charge positively, and increasing tendency to charge negatively 101.

The Melcann® Sand was significantly more homogenous in particle size and

composition than the Backyard Soil and hence it was expected that the sand would

provide clearer separation in the PCA scores plots (as was evident in the results). The

more homogenous the ground’s surface, the more likely the sample collected is

representative of the bulk. Conversely, the more heterogeneous the ground surface the

greater the number of samples that need to be collected from the impression. Chapter

2.7 explained that more samples were collected from the Backyard Soil than the

Melcann® Sand, however some ambiguity remained evident between the three shoe

types in the Backyard Soil PCA Plots (Figures 5.3b and 5.4b). It appears from the

separation evident in Figure 5.3 and 5.4, that homogeneity along with particle size, and

perhaps even electrostatic properties of the shoe sole play an important role during

particle transfer.

5.3.6 PROPOSED TRIBOELECTRIC EFFECT HYPOTHESIS

‘Triboelectric Effect’ describes the transfer of charge from one material to another

when two dissimilar materials come in contact 102. In practice, rubbing surfaces together

produces charging. For many materials, it is only necessary to touch the surfaces

together, then separate them to transfer a measurable charge 103. One of the materials

becomes positively charged and the other negatively charged giving an overall net

electric charge and the magnitude of the charge depends largely on the materials position

in the Triboelectric Series 101.

The Triboelectric Series is a list that ranks materials in order of their decreasing

tendency to charge positively (lose electrons), and increasing tendency to charge

negatively (gain electrons) such that in the middle of the series are materials that do not

show strong tendency to charge either way. A summarised Triboelectric Series

(containing materials relevant to this study) is displayed in Figure 5.5. A material towards

the bottom of the series, when touched to a material near the top of the series, will attain

a more negative charge, and vice versa. The further away two materials are from each

other on the series, the greater the charge transferred. While, materials near to each

other on the series may not exchange any charge.

It was not possible to confirm the material of each shoe sole used in this study

(Leather, Jogger and Walk Shoe) as the labelling inside the shoes was either worn and

illegible, or not present at all. The label inside the Leather Shoe was partially legible,

indicating a Leather Upper and Leather Sole. It is suspected that the Jogger and Walk

shoe soles are both Rubber, of some sort.

It is interesting to note, in Figure 5.5, Leather and Quartz/Silica are both located in

the series indicating a strong tendency to charge positively. Therefore, should these two

materials come into contact (like the Leather Shoe contacting the Melcann® Sand or

Backyard Soil) the overall net charge would be minimal. Conversely, Hard Rubber and

Synthetic Rubber are both located in the series as charging negatively. Should

Hard/Synthetic Rubber come into contact with Quartz/Silica (like the Jogger or Walk Shoe

contacting the Melcann® Sand or Backyard Soil), it is expected that a comparatively large

overall net charge would result.

This hypothesis can be expanded to provide a possible explanation as to why the

Jogger and Walk shoes separated largely on PC 2 in Figures 5.3 and 5.4 while the

Leather Shoe objects tended to remain around the origin. The contact of the Rubber with

the Quartz (present in the Melcann® Sand or Backyard Soil) created a large overall net

charge (compared to a small net charge created by the contact with the Leather Shoe). It

is proposed, the greater the overall net charge, the greater the ability to bind the sand/soil

particles to the shoe sole which has introduced a bias in the overall sample collected and

analysed.

It is well known that high moisture levels and humidity have the ability to void

electrostatic charge created as a result of the Triboelectric Effect. Therefore, the

moisture associated with the Wet Sampling method essentially neutralised any

electrostatic forces created when this sampling method was employed. The drying step

in the Wet Sampling method also essentially standardized the moisture content of the

samples (ie. the water content of the samples after drying was more consistent). The

consistency in moisture content meant less separation between the samples and hence

difficulty in establishing if trends (Figures 5.4a and 5.4b) or separation was evident. This

observation also suggests that moisture/humidity is an important factor that contributes to

the separation that can be seen between the various samples.

Many other factors such as sand/soil composition, particle composition, particle

size and shape and impurities contribute to the this effect 104 so much further work is

required to substantiate such a compound hypothesis.

Figure 5.6 – Sampling sites recorded during Leather Shoe contact with

Melcann® Sand using Wet Sampling method.

Sampling Site E

Sampling Site D

Sampling Site C

Sampling Site B

Sampling Site A

-10

-5

0

5

10

15

-12 -8 -4 0 4 8 12

PC 1 (16.3%)

PC

2 (

15.2

%)

Site A Site B Site C Site D Site E



Objects coloured according to sampling Site (See Figure 5.6).

signifies duplicate scans recorded from soil adhering to shoe

signifies duplicate scan of samples taken from impression.

▬▬ signifies mirror image line

5.3.7 SITE SPECIFIC CORRELATION

With such interesting trends emerging through the use of PCA, a further question

arose. Could PCA reveal information corresponding to the position where the samples

were collected, relating a specific site on the shoe to its correlating site on the

impression? For example, if soil adhered to the tip of the shoe during the simulated

walking motion and a comparative sample was collected from the tip of the impression

remaining in the soil, would any correlation be evident in the PCA scores plot? Using the

Dry Brushed method insufficient sand/soil adhered to the shoe to allow site specific

sampling. However, using the Wet Sampling method it was possible to collect samples

from a specific shoe sole site where sand/soil had adhered to the shoe and a sample

collected from the corresponding site in the soil impression.

Figure 5.7 displays the spectral objects collected from the Leather Shoe

contacting the Melcann® Sand surface according to the Wet Sampling method. The total

variance explained by this scores plot is 32% with PC 1 accounting for 16%. The objects

have been coloured according to the site on the Leather shoe where samples were

collected. Figure 5.6 illustrates the sites where the samples were collected. Duplicate

NIR spectra were recorded from all samples. Those objects, in Figure 5.7, grouped by an

oval shape signify the duplicate scans taken from the sand that adhered to the shoe,

while those objects grouped using a rectangle signify the duplicate scans taken from the

corresponding site on the impression. For example, the two Pink objects grouped by an

Oval represent the duplicate scans taken from the Sand adhering to the shoe at Sampling

Site E while the two Pink objects grouped by a Rectangle represent the duplicate scans

taken from the corresponding impression for Sampling Site E.

The spectra grouped using an oval (i.e. the sand adhering to the shoe) are

located negative on PC 1 and positive on PC 2 while those spectra grouped using a

rectangle (the sand from the impression) are generally located positive on PC 1 and

negative on PC 2. The correlation between the sites is most easily visualised by

imagining a diagonal line across the plot from the lower left to the upper right, such as

illustrated by the red line shown across Figure 5.7. The corresponding sample for each

site is located approximately opposite its partner according to the red line (E.g. Orange is

opposite its corresponding Orange, Green opposite Green and so on). It is also

interesting to note, that sampling site A (Orange) located at the heel of the foot

contributes to much of the positive variance on PC 2 while sampling site E (pink) located

at the toe of the foot contributes to much of the negative variance on PC 2. The two

sampling sites at the extremities of the shoe, Sites A and E (heel and toe respectively)

are located on opposite sides of the PCA scores plot.

Site specific PCA scores plots were created for both Backyard Soil and Melcann®

Sand using each of the three shoes and the Wet Sampling method. The trending was

more evident in the Sand plots than the Soil due to reasons discussed earlier (Chapter

5.3.5). The other plots have not been included here as they demonstrated similar results

as that already illustrated in Figure 5.6 and 5.7 (Refer to Appendix Figures 7.8-7.17 for

further plots not included here).

Chapter Summary

• Principal Component Analysis readily distinguished between the 4870-26

‘Backyard Soil’ and the 4870-27 ‘Melcann® Sand’ spectral objects as well as the

two sampling methods, Dry Brushed and Wet Sampled.

• The Wet Sampling method required a drying step, which lead to a greater

consistency in moisture content in those samples, which consequently lead to less

separation in the PCA scores plots displaying those objects sampled using this

method. Therefore it was concluded that the moisture content of the sand/soil

played an important role in distinguishing between the various spectra. Future

work would be beneficial in this area.

• Further PCA exploration revealed that separation was evident between the three

shoe types (Jogger, Leather or Walk Shoe) used during the various contacts with

the sand/soil suggesting that the properties of the shoe sole affected the transfer

of particles from the ground surface to the shoe. It was proposed that the

Triboelectric Effect along with sand/soil composition, moisture, particle size/shape

and impurities all play a significant role during the transfer of particles highlighting

another area for potential future work.

• The Melcann® Sand was significantly more homogenous in particle size and

composition than the Backyard Soil and as a result more samples needed to be

collected from the Soil (using the Dry Brushed Method). The heterogeneity of the

Backyard Soil made it difficult to collect representative soil samples and this was

evident in some PCA plots.

• Interestingly, it was possible to establish trends linking sand/soil adhered to a

specific site on the shoe to the corresponding site on the impression remaining in

the sand/soil using PCA.

• Overall, the results from this chapter are promising having successfully

demonstrated that NIR combined with Chemometrics can be applied to a ‘real

world’ forensic scenario providing information that can assist in establishing a link,

or lack thereof, between soil samples collected from shoe/s and the corresponding

impression.

References

107. Young, J.E. and W.E. Brownell, Moisture Expansion of Clay Products. Journal of

the American Ceramic Society, 2006. 42(12): p. 571-581.

108. Ireland, P.M., Contact charge accumulation and separation discharge Journal of

Electrostatics, 2009. 67: p. 462-467.

109. Lungu, M., Electrical separation of plastic materials using the triboelectric effect.

Minerals Engineering, 2004. 17: p. 69-75.

110. Diaz, A.F. and R.M. Felix-Navarro, A semi-quantitive tribo-electric series for

polymeric materials: the influence of chemical structure and properties. Journal of

Electrostatics, 2004. 62: p. 277-290.

111. Forward, K.M., D.J. Lacks, and Mohan R. Sankaran, Methodology for studying

particle–particle triboelectrification in granular materials. Journal of Electrostatics,

2009. 67: p. 178-183.

6.0 CONCLUDING REMARKS & FUTURE WORK

This investigation focused on the matching and discrimination of various soils

using NIR spectroscopy and interpreting the spectral differences with the aid of

Chemometrics and Multi-Criteria Decision Making Methods.

6.1 Concluding Remarks

6.1.1 INITIAL INVESTIGATION

• The 27 surface and core soil samples were obtained and successfully

characterised using XRD and ICP methods. The ICP results displayed acceptable

agreement with the complementary XRD results. These fundamental

characterisation results proved to be useful throughout the study by firstly making

it possible to select soils based on their chemical composition, and secondly, by

providing comparative analytical results to evaluate and interpret the NIR spectra.

• These soils were then analysed using NIR spectroscopy. The peaks observed in

the initial NIR results were assigned according to the literature. The peak

assignments were confirmed by comparing spectra collected from the various

soils with spectra collected from known minerals. The relative intensities of the

identified NIR absorptions reflected the quantitative XRD and ICP characterisation

results.

• PCA and FC analysis of the raw soils in the initial NIR investigation revealed that

the soils were primarily distinguished on the basis of their relative quartz and

kaolinte contents, and to a lesser extent on the horizon from which they were

obtained. This warranted a deeper investigation into the effect of quartz and

kaolinite on the NIR spectra and the resulting chemometrics interpretation.

• The investigation of the quartz-kaolinte mixed samples revealed that NIR

combined with PCA could distinguish the different kaolinites used in the study,

suggesting that the NIR spectral region was sensitive enough to contain

information describing variation within the kaolinite itself. It was also confirmed

that the intensity of the characteristic kaolinite absorptions were directly related to

the amount of kaolinite present in the soil.

• The temperature dependent NIR analysis confirmed the assignments of the

absorptions attributed to adsorbed and molecular bound water.

6.1.2 APPLICATION TO FORENSIC SCENARIO

• Using PCA, it was possible to distinguish between the Backyard Soil and the

Melcann® Sand spectra, as was expected. PCA also made it possible to

distinguish the two sampling methods (Dry Brushed and Wet Sampled) employed

in the study.

• Further PCA exploration revealed that it was possible to distinguish between the

three shoe types (ie, Jogger, Leather or Walk Shoe) used to simulate the walking

motion suggesting that there was some interaction (possibly based on the

Triboelectric Effect) between the soil and the shoe.

• Interestingly, it was possible to establish patterns that linked specific sampling

sites on the shoe to the corresponding site remaining in the impression.

• The moisture content of the samples played a vital role in the matching and

discrimination of the various spectra. The drying step required as part of the Wet

Sampling method essentially standardised the water content of samples obtained

in this manner. This resulted in less spectral differences between samples and

hence less separation in some PCA scores plots.

• It was established that the more heterogeneous the soil medium the more

samples should be collected to strengthen the Chemometrics analysis.

6.1.3 SUMMARY

Overall, it was established that NIR combined with Chemometrics is capable of

distinguishing between soil samples. This distinction was found to rely largely on the

relative amounts of quartz and kaolinite present within the soil, as well as the moisture

content of the soil. This knowledge was applied to a simulated forensic scenario with

promising results. The forensic application revealed some limitations of the process

relating to moisture content and homogeneity of the soil. These limitations can both be

overcome by simple sampling practices and maintaining the original integrity of the soil.

6.2 Future Work

The results obtained from the work detailed in this thesis potentially have a highly

beneficial application to forensic science. These preliminary investigations into the

capability of NIR combined with Chemometrics applied to soil samples have displayed

fundamental associations for matching and discriminating soils.

The benefits of NIR analysis are: minimal sample preparation; non-destructive;

can be completed in a short amount of time and potentially a portable device. In general,

many forensic laboratories throughout the world experience a significant backlog of

evidence often due to various reasons relating to the lengthy methods required to analyse

the evidence. Therefore, if a validated and adequate method involving NIR and

Chemometrics was developed through detailed scientific research, and was capable of

withstanding cross examination, the developed method may become accepted by the

court of law. In order for this possibility to eventuate much further work remains to be

completed.

Research performed thus far has not been performed with a portable NIR as

access to such an instrument was not possible. A portable NIR instrument would be

beneficial for onsite analysis with minimal disturbance to the soil impression and without

concern of interfering with the chain of evidence where sampling and transportation

would be required. Simulating a ‘real world’ scenario where a series of mock crime

scenes are established, the portable NIR instrument employed and data analysed using

chemometrics, is yet to be performed.

Further work is required into the effect that moisture content has on the NIR

spectra. This was briefly touched on in this thesis but additional analysis to determine

moisture content and possibly including Differential Thermal Analysis (DTA) of various

soils and comparing the results using Chemometrics methods could prove to be

promising. This method alone could potentially exhibit a novel application for matching

and discriminating soils.

The work thus far has also been limited by the dimensions of the ‘blue box’

(Figure 2.1) in which the sample was contained. It is important to remember that the soil

which makes up the impression is not the only soil which can provide crucial evidentiary

information. It is necessary also to take into account the soil in surrounding areas.

Another expansion would also be to include a greater selection of soils encompassing a

broader range of mineral compositions as the soils in this study were mainly composed of

quartz and kaolinite.

The most interesting and unexpected result to come from this research was the

ability to discriminate between the various shoes used during the forensic simulation. As

suggested in Chapter 5.3, the properties of the shoe sole affect the transfer of particles

from the ground surface to the shoe. It was proposed that the Triboelectric Effect,

moisture, particle size/shape and composition all play a significant role during the transfer

of particles. Further work in this area is required to substantiate these claims.

7.0 APPENDIX

Table 7.1 – Soil Classifications according to Al-Shiekh Khalil et al 69.

Shading indicates those soils used in Section 2.6, Temperature Dependent Analysis.

Sample Number

Site Number

Horizon Soil Type

4870-1 1 B2 Yellow Dermosol

4870-2 2 A Grey Chromosol

4870-3 4 B2 Grey Chromosol

4870-4 5 A Red Dermosol

4870-5 7 B1 Grey Kurosol

4870-6 8 A Yellow Dermosol

4870-7 9 A Yellow Chromosol


4870-9 12 A Bleached Leptic Tenosol


4870-11 15 A Yellow Dermosol

4870-12 18 A Yellow Kandosol

4870-13 19 A Yellow Kandosol

4870-14 20 A Red Vertosol

4870-15 26 B2 Red Kandosol


4870-17 31 B1 Grey Sodosol

4870-18 33 B2 Red Sodosol

4870-19 36 B2 Red Sodosol

4870-20 38 B1 Rudosol

4870-21 40 B2 Brown Kandosol

4870-22 42 B2 Grey Kurosol

4870-23 43 B1 Yellow Sodosol

4870-24 47 B2 Red Dermosol

4870-25 48 B1 Brown Chromosol

Table 7.2 - Sample information and file names for Dry Brushed sampling method.

Sample Number

Vial Number

Location Impression/

Shoe Shoe

Soil/ Sand

File Name

Duplicate

1 - Shoe Sand 1Sa 1Sb

2 Fore Cntr Impression Sand 1Fa 1Fb I

3 Rear Cntr Impression

Leather

Sand 1Ra 1Rb


5 Fore Cntr Impression Sand 2Fa 2Fb II


Jogger

Sand 2Ra 2Rb


8 Fore Cntr Impression Sand 3Fa 3Fb III


Walk

Sand 3Ra 3Rb

10 - Shoe Soil 4Sa 4Sb

11 A Impression Soil 4Aa 4Ab

12 B Impression Soil 4Ba 4Bb

13 C Impression Soil 4Ca 4Cb

14 D Impression Soil 4Da 4Db

IV

15 E Impression

Leather

Soil 4Ea 4Eb






V

21 E Impression

Jogger

Soil 5Ea 5Eb






VI

27 E Impression

Walk

Soil 6Ea 6Eb

Figure 7.1 – Sampling sites for the Leather Shoe (Sample number I Table 7.2) contacting with 4870-27 Melcann® Sand using the Dry Brushed sampling method. Vial number 1

corresponds to the brushed sample collected from the sand particles that adhered to the Leather shoe sole during contact. Vial Numbers 2 & 3 sampling sites are displayed on

the below shoe diagram.

Vial 2 – Fore foot

Vial 3 – Rear foot

Figure 7.2 – Sampling sites for the Jogger Shoe (Sample number II Table 7.2) contacting

with 4870-27 Melcann® Sand using the Dry Brushed sampling method. Vial number 4 corresponds to the brushed sample collected from the sand particles that adhered to the sole of the Jogger during contact. Vial Numbers 5 & 6 sampling sites are displayed on

the below shoe diagram.



Figure 7.3 – Sampling sites for the Walk Shoe (Sample number III Table 7.2) contacting with 4870-27 Melcann® Sand using the Dry Brushed sampling method. Vial number 7

corresponds to the brushed sample collected from the sand particles that adhered to the Walk shoe sole during contact. Vial Numbers 8 & 9 sampling sites are displayed on the

below shoe diagram.



Figure 7.4 – Sampling sites for the Leather Shoe (Sample number IV Table 7.2) contacting with 4870-26 Backyard Soil using the Dry Brushed sampling method. Vial number 10 corresponds to the brushed sample collected from the soil particles that

adhered to the Leather shoe sole during contact.

Site A

Site E

Site D

Site C

Site B

Figure 7.5 – Sampling sites for the Jogger Shoe (Sample number V Table 7.2) contacting with 4870-26 Backyard Soil using the Dry Brushed sampling method. Vial number 16 corresponds to the brushed sample collected from the soil particles that

adhered to the sole of the Jogger during contact.

Site A

Site D

Site C

Site E

Site B

Figure 7.6 – Sampling sites for the Walk Shoe (Sample number VI Table 7.2) contacting with 4870-26 Backyard Soil using the Dry Brushed sampling method. Vial number 22

corresponds to the brushed sample collected from the soil particles that adhered to the sole of the Jogger during contact.

Site C

Site D

Site A

Site E

Site B

Table 7.3 - Sample information and file names for Wet sampled, Oven Dried method.

Sample Number

Vial Number

Map Location

Impression/ Shoe

Shoe Soil/ Sand

File Name Duplicate

28 A Shoe Sand 7DSAa 7DSAb

29 B Shoe Sand 7DSBa 7DSBb

30 C Shoe Sand 7DSCa 7DSCb

31 D Shoe Sand 7DSDa 7DSDb

32 E Shoe Sand 7DSEa 7DSEb

33 A Impression Sand 7DIAa 7DIAb

34 B Impression Sand 7DIBa 7DIBb

35 C Impression Sand 7DICa 7DICb

36 D Impression Sand 7DIDa 7DIDb

VII

37 E Impression

Leather

Sand 7DIEa 7DIEb










VIII

47 E Impression

Jogger

Sand 8DIEa 8DIEb










IX

57 E Impression

Walk

Sand 9DIEa 9DIEb

58 A Shoe Soil 10DSAa 10DSAb

59 B Shoe Soil 10DSBa 10DSBb

60 C Shoe Soil 10DSCa 10DSCb

61 D Shoe Soil 10DSDa 10DSDb

62 E Shoe Soil 10DSEa 10DSEb

63 A Impression Soil 10DIAa 10DIAb

64 B Impression Soil 10DIBa 10DIBb

65 C Impression Soil 10DICa 10DICb

X

66 D Impression

Leather

Soil 10DIDa 10DIDb

67 E Impression Soil 10DIEa 10DIEb









76 D Impression Soil 11DIDa 11DIDb

XI

77 E Impression

Jogger

Soil 11DIEa 11DIEb









86 D Impression Soil 12DIDa 12DIDb

XII

87 E Impression

Walk

Soil 12DIEa 12DIEb

Figure 7.7 – Sampling sites for the Leather Shoe (Sample number VII Table 7.3) contacting with 4870-27 Melcann® Sand using the Wet Sampled Oven Dried sampling method. Samples were collected directly from the shoe at the sites indicated below, as

well as the corresponding sites remaining in the impression. Sampling sites were selected according to where the sand adhered to the shoe.

Site C

Site A

Site E

Site D

Site B

Figure 7.8 – Sampling sites for the Jogger Shoe (Sample number VIII Table 7.3) contacting with 4870-27 Melcann® Sand using the Wet Sampled Oven Dried sampling method. Samples were collected directly from the shoe at the sites indicated below, as


Site D

Site A

Site E

Site C

Site B

-10

-8

-6

-4

-2

0

2

4

6

8

10

-15 -10 -5 0 5 10 15

PC1 (18.5%)

PC

2 (

12.5

%)







Figure 7.10 – Sampling sites for the Walk Shoe (Sample number IX Table 7.3) contacting with 4870-27 Melcann® Sand using the Wet Sampled Oven Dried sampling method. Samples were collected directly from the shoe at the sites indicated below, as


Site E

Site D

Site C

Site A

Site B

-12

-10

-8

-6

-4

-2

0

2

4

6

8

-15 -10 -5 0 5 10 15

PC1 (16.6%)

PC

2 (

13.0

%)







Figure 7.12 – Sampling sites for the Leather Shoe (Sample number X Table 7.3) contacting with 4870-26 Backyard Soil using the Wet Sampled Oven Dried sampling

method. Samples were collected directly from the shoe at the sites indicated below, as well as the corresponding sites remaining in the impression. Sampling sites were

selected according to where the soil adhered to the shoe.

Site C

Site A

Site B

Site D

Site E

-10

-5

0

5

10

15

-15 -10 -5 0 5 10 15

PC1 (20.6%)

PC

2 (

15.3

%)







Figure 7.14 – Sampling sites for the Jogger Shoe (Sample number XI Table 7.3) contacting with 4870-26 Backyard Soil using the Wet Sampled Oven Dried sampling



Site D

Site C

Site C

Site A

Site E

-10

-8

-6

-4

-2

0

2

4

6

8

-15 -10 -5 0 5 10 15 20 25

PC1 (28.4%)

PC

2 (

13.4

%)







Figure 7.16 – Sampling sites for the Walk Shoe (Sample number XII Table 7.3) contacting with 4870-26 Backyard Soil using the Wet Sampled Oven Dried sampling



Site D

Site B

Site E

Site A

Site C

-8

-6

-4

-2

0

2

4

6

8

-20 -15 -10 -5 0 5 10 15 20 25

PC1 (24.9%)

PC

2 (

11.9

%%

)


`

`






Figure 7.18 – ICP Experimental Calibration Plot for SiO2.

Calibration of SiO2

(251.611nm)y = 569.49302x + 111.47166

R2 = 0.99996

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00

Concentration (wt. %)

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Table 7.4 – ICP Experimental Calibration Data for SiO2. (Shading indicates standard was discarded as an outlier.)

SiO2 251.611nm

Std. Conc. (wt. %)

Instrument Response

Calc. Conc. (wt. %)

Error % Error

Blank 0.00 56.4 -0.097 -0.097 Std. 1 (353) 41.49 23721.4 41.458 -0.032 -0.078 Std. 2 (446) 46.11 26534.1 46.397 0.287 0.622 Std. 3 (1552) 61.67 36219.6 63.404 1.734 2.812 Std. 4 (2796) 75.30 42904.4 75.142 -0.158 -0.210

Slope 569.49

y-intercept 111.47 R2 0.99996

Figure 7.19 – ICP Experimental Calibration Plot for CaO.

Calibration of CaO(317.933nm) y = 3016.84980x + 470.14861

R2 = 0.99936

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

55000

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00


Ins

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Table 7.5 – ICP Experimental Calibration Data for CaO. CaO 317.933nm

Std. Conc. (wt. %)

Instrument Response

Calc. Conc. (wt. %)

Error % Error

Blank 0.00 254.6 -0.07 -0.07 Std. 1 (353) 17.05 51748.8 17.00 -0.05 -0.308 Std. 2 (446) 11.42 34672 11.34 -0.08 -0.727 Std. 3 (1552) 6.05 19695.1 6.37 0.32 5.331 Std. 4 (2796) 0.92 2897.4 0.80 -0.12 -12.547

Slope 3016.85

y-intercept 470.15 R2 0.99936

Figure 7.20 – ICP Experimental Calibration Plot for Fe2O3.

Calibration of Fe2O3

(259.940nm) y = 4013.62873x + 700.26684

R2 = 0.99839

0

10000

20000

30000

40000

50000

60000

70000

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00


Ins

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Table 7.6 – ICP Experimental Calibration Data for Fe2O3.

Fe2O3 w. %

259.940nm

Std. Conc. (wt. %)

Instrument Response

Calc. Conc. (wt. %)

Error % Error


Slope 4013.63

y-intercept 700.27 R2 0.99839

Figure 7.21 – ICP Experimental Calibration Plot for MnO.

Calibration of MnO(257.610nm) y = 48453.54916x + 21.99104

R2 = 0.99980

0

2000

4000

6000

8000

10000

12000

0.000 0.050 0.100 0.150 0.200 0.250


Ins

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Table 7.7 – ICP Experimental Calibration Data for MnO. (Shading indicates standard was discarded as an outlier.)

MnO 257.610nm

Std.

Conc. (wt. %)

Instrument Response

Calc. Conc. (wt. %)

Error % Error

Blank 0.000 45.1 0.000 0.000 Std. 1 (353) 0.200 9611.7 0.198 -0.002 -1.042 Std. 2 (446) 0.233 11400.3 0.235 0.002 0.785 Std. 3 (1552) 0.174 8888.9 0.183 0.009 5.171 Std. 4 (2796) 0.042 2046.3 0.042 0.000 -0.528

Slope 48453.55

y-intercept 21.99 R2 0.99980

Figure 7.22 – ICP Experimental Calibration Plot for Al2O3.

Calibration of Al2O3

(394.401nm) y = 1741.35791x + 73.21824R2 = 0.99999

0

5000

10000

15000

20000

25000

30000

35000

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00


Ins

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Table 7.8 – ICP Experimental Calibration Data for Al2O3. Al2O3 394.401nm

Std.

Conc. (wt. %)

Instrument Response

Calc. Conc. (wt. %)

Error % Error

Blank 0.00 74.9 0.00 0.001 Std. 1 (353) 15.59 27231 15.60 0.006 0.037 Std. 2 (446) 15.90 27688.1 15.86 -0.042 -0.263 Std. 3 (1552) 17.50 30593.1 17.53 0.026 0.151 Std. 4 (2796) 12.95 22638.7 12.96 0.009 0.066

Slope 1741.36

y-intercept 73.22 R2 0.99999

Figure 7.23 – ICP Experimental Calibration Plot for TiO2.

Calibration of TiO2

(336.122nm) y = 23555.96809x - 212.29898

R2 = 0.99931

0

5000

10000

15000

20000

25000

30000

35000

40000

0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75


Ins

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Table 7.9 – ICP Experimental Calibration Data for TiO2. TiO2

336.122nm

Std.

Conc. (wt. %)

Instrument Response

Calc. Conc. (wt. %)

Error % Error

Blank 0.00 207.4 0.00 0.00 Std. 1 (353) 1.48 35174.1 1.48 0.00 0.284 Std. 2 (446) 1.62 37694.1 1.59 -0.03 -1.779 Std. 3 (1552) 0.62 13856.3 0.58 -0.04 -6.578 Std. 4 (2796) 0.20 4346.0 0.18 -0.02 -12.258

Slope 23555.97

y-intercept 212.30 R2 0.99931

Figure 7.24 – ICP Experimental Calibration Plot for K2O.

Calibration of K2O(766.491nm) y = 7115.70431x - 86.44256

R2 = 0.99965

0.0

5000.0

10000.0

15000.0

20000.0

25000.0

30000.0

35000.0

0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00


Ins

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Table 7.10 – ICP Experimental Calibration Data for K2O. (Shading indicates

standard was discarded as an outlier.) K2O 766.491nm

Std.

Conc. (wt. %)

Instrument Response

Calc. Conc. (wt. %)

Error % Error

Blank 0.00 160.1 0.03 0.035 Std. 1 (353) 0.19 1314.6 0.20 0.007 3.629 Std. 2 (446) 1.16 7772.3 1.10 -0.056 -4.791 Std. 3 (1552) 1.92 14214.8 2.01 0.090 4.678 Std. 4 (2796) 4.50 32034.1 4.51 0.014 0.312

Slope 7115.70

y-intercept -86.44 R2 0.99965

Figure 7.25 – ICP Experimental Calibration Plot for BaO.

Calibration of Ba(455.403nm) y = 22.45763x + 96.97824

R2 = 0.99909

0.0

1000.0

2000.0

3000.0

4000.0

5000.0

6000.0

7000.0

8000.0

9000.0

10000.0

11000.0

0.0 50.0 100.0 150.0 200.0 250.0 300.0 350.0 400.0 450.0 500.0

Concentration (mg/L)

Ins

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Table 7.11 – ICP Experimental Calibration Data for BaO. (Shading indicates standard was discarded as an outlier.)

Ba 455.403nm

Std.

Conc. (wt. %)

Instrument Response

Calc. Conc. (wt. %)

Error % Error

Blank 0.0 133.0 1.6 1.604 Std. 1 (353) 53.5 1195.1 48.9 -4.603 -8.603 Std. 2 (446) 478.0 10694.0 471.9 -6.133 -1.283 Std. 3 (1552) 353.0 8396.1 369.5 16.546 4.687 Std. 4 (2796) 348.0 8117.3 357.1 9.131 2.624

Slope 22.46

y-intercept 96.98 R2 0.99909

Figure 7.26 – ICP Experimental Calibration Plot for MgO.

Calibration of MgO(285.213nm) y = 8636.68798x + 315.54638

R2 = 0.99943

0.0

5000.0

10000.0

15000.0

20000.0

25000.0

30000.0

35000.0

40000.0

45000.0

50000.0

55000.0

60000.0

65000.0

70000.0

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00


Ins

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Table 7.12 – ICP Experimental Calibration Data for MgO. MgO

285.213nm

Std.

Conc. (wt. %)

Instrument Response

Calc. Conc. (wt. %)

Error % Error


Slope 8636.69

y-intercept 315.55 R2 0.99943

Figure 7.27 – ICP Experimental Calibration Plot for Na2O.

Calibration of Na2O(588.995nm)

y = 120870.07413x + 3950.43712R2 = 0.99959

0.0

50000.0

100000.0

150000.0

200000.0

250000.0

300000.0

350000.0

400000.0

450000.0

500000.0

0 0.5 1 1.5 2 2.5 3 3.5 4


Ins

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Table 7.13 – ICP Experimental Calibration Data for Na2O. (Shading indicates standard was discarded as an outlier.)

Na2O 588.995nm

Std.

Conc. (wt. %)

Instrument Response

Calc. Conc. (wt. %)

Error % Error

Blank 0.00 7803.2 0.03 0.032 Std. 1 (353) 0.95 114383.5 0.91 -0.036 -3.826 Std. 2 (446) 2.82 342228.4 2.80 -0.021 -0.756 Std. 3 (1552) 3.67 450660.0 3.70 0.026 0.703 Std. 4 (2796) 3.77 476116.4 3.91 0.136 3.618

Slope 120870.07

y-intercept 3950.44 R2 0.99959

Figure 7.28 – ICP Experimental Calibration Plot for SrO.

Calibration of Sr(407.771nm) y = 77.51308x + 3459.97861

R2 = 0.99887

0.0

10000.0

20000.0

30000.0

40000.0

50000.0

60000.0

70000.0

80000.0

0.0 100.0 200.0 300.0 400.0 500.0 600.0 700.0 800.0 900.0 1000.0

Concentration (mg/L)

Ins

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on

se

Table 7.14 – ICP Experimental Calibration Data for SrO. (Shading indicates standard was discarded as an outlier.)

Sr 407.771nm

Std.

Conc. (wt. %)

Instrument Response

Calc. Conc. (wt. %)

Error % Error

Blank 0.00 2875.3 -7.5 -7.543 Std. 1 (353) 823.0 66789.7 817.0 -5.980 -0.727 Std. 2 (446) 958.0 77114.0 950.2 -7.786 -0.813 Std. 3 (1552) 581.0 50146.8 602.3 21.309 3.668 Std. 4 (2796) 92.8 14116.4 137.5 44.679 48.145

Slope 77.51

y-intercept 3459.98 R2 0.99887

Figure 7.29 – ICP Experimental Calibration Plot for P2O5.

Calibration of P2O5

(177.495nm) y = 557.48000x + 1.22945R2 = 0.99949

0.000

50.000

100.000

150.000

200.000

250.000

300.000

350.000

400.000

450.000

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80


Ins

tru

me

nt

Re

sp

on

se

Table 7.15 – ICP Experimental Calibration Data for P2O5. P2O5

177.495nm

Std.

Conc. (wt. %)

Instrument Response

Calc. Conc. (wt. %)

Error % Error

Blank 0.00 -0.269 0.00 -0.003 Std. 1 (353) 0.10 61.840 0.11 0.009 8.722 Std. 2 (446) 0.71 398.100 0.71 0.002 0.268 Std. 3 (1552) 0.28 152.900 0.27 -0.008 -2.834

Slope 557.48

y-intercept 1.23 R2 0.99949

Tab

le 7

.16 –

ICP

exp

erim

enta

l Cer

tifie

d R

efer

ence

Mat

eria

l 270

4 (B

uffa

lo R

iver

Sed

imen

t) d

ata.

(S

hadi

ng d

enot

es m

easu

rem

ent u

nits

mg/

L no

t wt.

%.)

A

l 2O3

(wt.%

) C

aO

(wt.%

) F

e 2O

3

(wt.%

) M

nO

(wt.%

) S

iO2

(wt.%

) T

iO2

(wt.%

) K

2O

(wt.%

) B

a (m

g/L)

M

gO

(wt.%

) N

a 2O

(w

t.%)

Sr

(mg/

L)

P2O

5

(wt.%

)

CR

M 2

704-

A

11.5

6 3.

61

5.88

0.

075

62.2

3 0.

750

2.53

40

2.00

2.

06

0.88

10

9.20

0.

245

CR

M 2

704-

B

11.5

1 3.

63

5.88

0.

074

62.3

1 0.

749

2.54

40

3.50

2.

10

0.95

11

1.40

0.

247

CR

M 2

704-

C

11.3

5 3.

62

5.96

0.

076

61.6

6 0.

755

2.44

40

4.80

2.

06

0.90

11

0.10

0.

234

CR

M 2

704

Avg

11

.47

3.62

5.

91

0.07

5 62

.07

0.75

1 2.

50

403.

43

2.07

0.

89

110.

23

0.24

2 T

ab

le 7

.17 –

Com

paris

on o

f exp

erim

enta

l Cer

tifie

d R

efer

ence

Mat

eria

l 270

4 (B

uffa

lo R

iver

Sed

imen

t) m

easu

red

resu

lts w

ith c

ertif

ied

valu

es. (

Sha

ding

de

note

s m

easu

rem

ent u

nits

mg/

L no

t wt.

%.)

A

l (w

t. %

) C

a (w

t.%)

Fe

(wt.%

) M

n (w

t.%)

Si

(wt.%

) T

i (w

t.%)

K

(wt.%

) B

a (m

g/L)

M

g (w

t.%)

Na

(wt.%

) S

r (m

g/L)

P

(wt.%

)

Exp

erim

enta

l Res

ults

CR

M 2

704

6.07

2.

59

4.13

57

3 29

.01

0.45

0 2.

07

403

1.24

0.

66

110

0.10

5 C

ertif

ied

CR

M 2

704

6.11

2.

60

4.11

55

5 29

.08

0.45

7 2.

00

414

1.20

0.

55

130

0.09

9 E

rror

-0

.04

-0.0

1 0.

02

18.0

-0

.07

-0.0

1 0.

07

-11.

0 0.

04

0.11

-2

0.0

0.01

%

Err

or

-0.6

5 -0

.38

0.49

3.

24

-0.2

4 -1

.53

3.50

-2

.66

3.33

20

.7

-15.

4 6.

28

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