DEEP RAMAN SPECTROSCOPY IN THE ANALYTICAL ......5. Izake E, Sundarajoo S, Olds W, Cletus B, Jaatinen...

DEEP RAMAN SPECTROSCOPY IN THE

ANALYTICAL FORENSIC INVESTIGATION

OF CONCEALED SUBSTANCES

Shankaran Sundarajoo

BSc (Chemistry)

Principal Supervisor: Dr Emad Kiriakous

Submitted in partial fulfilment of the requirements for the degree of

Master of Applied Science (Research)

Science & Engineering Faculty

Queensland University of Technology

September 2012

‎Chapter 1: Introduction 1

Keywords

Concealed Substance, Depth Profiling, Deep Raman Spectroscopy, Spatially-Offset

Raman Spectroscopy (SORS), Time-Resolved Spatially-Offset Raman spectroscopy,

Time –Resolved Raman Spectroscopy


Abstract

Deep Raman Spectroscopy is a domain within Raman spectroscopy consisting

of techniques that facilitate the depth profiling of diffusely scattering media. Such

variants include Time-Resolved Raman Spectroscopy (TRRS) and Spatially-Offset

Raman Spectroscopy (SORS). A recent study has also demonstrated the integration

of TRRS and SORS in the development of Time-Resolved Spatially-Offset Raman

Spectroscopy (TR-SORS).

This research demonstrates the application of specific deep Raman

spectroscopic techniques to concealed samples commonly encountered in forensic

and homeland security at various working distances. Additionally, the concepts

behind these techniques are discussed at depth and prospective improvements to the

individual techniques are investigated. Qualitative and quantitative analysis of

samples based on spectral data acquired from SORS is performed with the aid of

multivariate statistical techniques. By the end of this study, an objective comparison

is made among the techniques within Deep Raman Spectroscopy based on their

capabilities.

The efficiency and quality of these techniques are determined based on the

results procured which facilitates the understanding of the degree of selectivity for

the deeper layer exhibited by the individual techniques relative to each other. TR-

SORS was shown to exhibit an enhanced selectivity for the deeper layer relative to

TRRS and SORS whilst providing spectral results with good signal-to-noise ratio.

Conclusive results indicate that TR-SORS is a prospective deep Raman technique

that offers higher selectivity towards deep layers and therefore enhances the non-

invasive analysis of concealed substances from close range as well as standoff

distances.


Table of Contents

Keywords ................................................................................................................................................ 1

Abstract ................................................................................................................................................... 2

Table of Contents .................................................................................................................................... 3

List of Publications ................................................................................................................................. 5

List of Figures ......................................................................................................................................... 6

List of Tables ........................................................................................................................................ 11

List of Abbreviations ............................................................................................................................. 12

Statement of Original Authorship ......................................................................................................... 13

Acknowledgements ............................................................................................................................... 14

CHAPTER 1: INTRODUCTION ..................................................................................................... 15

1.1 Background ................................................................................................................................ 15

1.2 Scope .......................................................................................................................................... 16

1.3 Objectives .................................................................................................................................. 16

1.4 Thesis Outline ............................................................................................................................ 17

CHAPTER 2: LITERATURE REVIEW ......................................................................................... 19

2.1 Introduction ................................................................................................................................ 19

2.2 Explosives .................................................................................................................................. 20 2.2.1 Need for Selectivity ........................................................................................................ 20 2.2.2 Safety and Distance ........................................................................................................ 21

2.3 Chemical Warfare Agents (CWA) ............................................................................................. 22

2.4 Illicit Drugs and Counterfeit pharmaceutical products .............................................................. 22

2.5 Existing bulk Detection techniques ............................................................................................ 23 2.5.1 Nuclear techniques.......................................................................................................... 24 2.5.2 X-ray Based Detection Techniques ................................................................................ 25 2.5.3 Laser Based Techniques ................................................................................................. 26

2.6 Deep Raman Spectroscopy ........................................................................................................ 31 2.6.1 Photon Migration in Diffusely Scattering Media ............................................................ 31 2.6.2 Existing Techniques in Deep Raman Spectroscopy ....................................................... 32

CHAPTER 3: EXPERIMENTAL DESIGN ..................................................................................... 34

3.1 Instrumentation .......................................................................................................................... 34 3.1.1 Stand-off pulsed TRRS / SORS / TR-SORS .................................................................. 34 3.1.2 Continuous Wave (CW) SORS Detection at 6cm .......................................................... 37 3.1.3 TR-SORS Detection at 6cm ............................................................................................ 38

3.2 Chemicals................................................................................................................................... 39

CHAPTER 4: TIME-RESOLVED RAMAN SPECTROSCOPY .................................................. 43

4.1 Introduction ................................................................................................................................ 43

4.2 Aims ........................................................................................................................................... 44

4.3 Concept of TRRS ....................................................................................................................... 45

4.4 Stand-off TRRS Detection Study ............................................................................................... 49 4.4.1 Preliminary TRRS Analysis............................................................................................ 49


4.4.2 Stand-off TRRS Detection at 3 metres ........................................................................... 57 4.4.3 Stand-off TRRS Detection at 15 metres ......................................................................... 60

4.5 Conclusion ................................................................................................................................. 62

CHAPTER 5: SPATIALLY-OFFSET RAMAN SPECTROSCOPY ............................................ 63

5.1 Introduction ................................................................................................................................ 63 5.1.1 Conventional Continuous Wave (CW) SORS ................................................................ 63 5.1.2 Inverse SORS ................................................................................................................. 66 5.1.3 Transmission Raman Spectroscopy ................................................................................ 66 5.1.4 Applications of CW SORS ............................................................................................. 66 5.1.5 Pulsed Wave (PW) SORS ............................................................................................... 68

5.2 Aims ........................................................................................................................................... 68

5.3 Continuous Wave (CW) SORS Analysis ................................................................................... 69 5.3.1 Demonstration of CW SORS Data Treatment ................................................................ 69 5.3.2 CW SORS Detection of Concealed Substances under Background Lighting ................. 72 5.3.3 Qualitative and Semi-Quantitative Analysis of CW SORS Spectral Data using

Chemometrics ................................................................................................................. 74

5.4 Stand-off SORS Detection Study ............................................................................................... 88 5.4.1 Preliminary SORS Analysis............................................................................................ 88 5.4.2 Standoff SORS Detection at 3 meters ............................................................................. 96 5.4.3 Stand-off SORS Detection at 15 metres ......................................................................... 99

5.5 Conclusion ............................................................................................................................... 101

CHAPTER 6: TIME-RESOLVED SPATIALLY OFFSET RAMAN SPECTROSCOPY ........ 102

6.1 Introduction .............................................................................................................................. 102

6.2 Aims ......................................................................................................................................... 103

6.3 Concept Of TR-SORS .............................................................................................................. 103

6.4 Stand-off TR-SORS DETECTIOn Study ................................................................................ 105 6.4.1 Preliminary Analysis .................................................................................................... 105 6.4.2 Stand-off TR-SORS Detection at 3 metres ................................................................... 115 6.4.3 Stand-off TR-SORS Detection at 15 metres ................................................................. 118

6.5 TR-SORS Detection at 6CM.................................................................................................... 120 6.5.1 TR-SORS Detection of Samples Concealed in Non-coloured Packaging Materials .... 121 6.5.2 TR-SORS Detection of Samples Concealed in Coloured Packaging Materials ........... 122

6.6 Conclusion ............................................................................................................................... 123

CHAPTER 7: SUMMARY .............................................................................................................. 124

7.1 Conclusions .............................................................................................................................. 124

7.2 Recommendations for further Research ................................................................................... 125

REFERENCES .................................................................................................................................. 126


List of Publications

1. Olds W, Sundarajoo S, Selby M, Cletus B, Fredericks P, Izake E. Non-invasive,

quantitative analysis of drug mixtures in containers using spatially offset Raman

spectroscopy (SORS) and multivariate statistical analysis. Applied Spectroscopy, 2012,

66(5), p.530-7.

2. Izake E, Cletus B, Olds W, Sundarajoo S, Fredericks P, Jaatinen E. Deep Raman

spectroscopy for the non-invasive standoff detection of concealed chemical threat

agents. Talanta, 2012, 94, p.342-347

3. Cletus B, Olds W, Izake E, Sundarajoo S, Fredericks P, Jaatinen E. Combined time- and

space-resolved Raman spectrometer for the non-invasive depth profiling of chemical

hazards. Analytical and Bioanalytical Chemistry, 2012, 403(1):1-9.

4. Cletus B, Olds W, Kiriakous E, Sundarajoo S, Fredericks P, Jaatinen E. Field portable

time resolved SORS sensor for the identification of concealed hazards. Next-Generation

Spectroscopic Technologies V, 2012, Baltimore, USA, Proceedings of SPIE 8374.

5. Izake E, Sundarajoo S, Olds W, Cletus B, Jaatinen E, Fredericks P. Standoff Raman

spectrometry for the non-invasive detection of explosives precursors in highly

fluorescing packaging. Talanta, 2013, 103, p.20-27


List of Figures

Figure 2.1: Scattering phenomena as a result of monochromatic excitation

of a sample.

Figure 2.2: Photon propagation profile as a result of photon diffusion

Figure 2.3: Existing variants within Deep Raman Spectroscopy

Figure3.1: Schematic instrumental configuration of the stand-off deep

Raman spectrometer

Figure 3.2: Stand-off detection performed at (a) 3m, (b) 8m and (c) 15m

Figure 3.3: Schematic diagram of CW inverse-SORS configuration

Figure 3.4: Schematic diagram of the TR-SORS instrumentation

Figure 3.5: Raman spectrum of 2,2-thiodiethanol

Figure 3.6: Raman spectrum of 2,4-dinitrotoluene

Figure 3.7: Raman spectrum of ammonium nitrate

Figure 3.8: Raman spectrum of aspirin

Figure 3.9: Raman spectrum of GBL

Figure 3.10: Raman spectrum of hydrogen peroxide

Figure 3.11: Raman spectrum of nitromethane

Figure 4.1: Temporal profile of Raman photons and fluorescence arising

from a two-layered diffusely scattering medium

Figure 4.2: Temporal profiles of Raman photons from the surface and

deeper layers of a sample at different stages of an impinging laser pulse

Figure 4.3: Raman spectra of ammonium nitrate concealed in the white

container acquired from 25ns to 65ns

Figure 4.4: TRRS of ammonium nitrate in a white HDPE container

Figure 4.5: Signal intensity ratio as a function of gate delays for the TRRS

analysis of ammonium nitrate concealed in a white HDPE container

Figure 4.6: Signal-to-noise ratio as a function of gate delays for the TRRS

analysis of ammonium nitrate concealed in a white HDPE container

Figure 4.7: TRRS analysis of ammonium nitrate in a yellow polystyrene

container

Figure 4.8: Signal intensity ratio as a function of gate delays for the TRRS

analysis of ammonium nitrate concealed in a yellow polystyrene container


Figure 4.9: Signal to noise ratio as a function of gate delays for the TRRS

analysis of ammonium nitrate concealed in a yellow polystyrene container

Figure 4.10: Demonstration of a scaled subtraction between two spectra

obtained at different gate delays for ammonium nitrate concealed in a

yellow polystyrene container

Figure 4.11: TRRS spectrum of ammonium nitrate detected from 8

metres. A scaled subtraction was performed between spectra obtained at

gate delays of 76ns and 79ns

Figure 4.12: TRRS analysis of aspirin concealed in a white HDPE

container

Figure 4.13: TRRS analysis of 2,2-thiodiethanol concealed in a white

HDPE container

Figure 4.14: TRRS analysis of GBL concealed in a white HDPE container

Figure 4.15: TRRS analysis of hydrogen peroxide concealed in a white

HDPE container

Figure 4.16: TRRS analysis of 2,4-DNT concealed in a white HDPE

container

Figure 4.17: TRRS analysis of nitromethane concealed in a white HDPE

container

Figure 4.18: TRRS analysis of ammonium nitrate concealed in a white

HDPE container

Figure 5.1: Illustration of the spatial effects of Raman photons undergoing

diffused scattering in a two layered diffusely scattering medium

Figure 5.2: Demonstration of spot and ring measurements using CW

SORS

Figure 5.3: Demonstration of a scaled subtraction to retrieve a clean

spectrum of the concealed layer

Figure 5.4: CW SORS spectra of a) Ammonium nitrate in an off-white

plastic bottle (measured under fluorescent light, SNR=10); b) H2O2 in an

off-white shampoo plastic bottle (measured under incandescent

background light, SNR=2); c); H2O2 in a red plastic bottle (measured

under incandescent background light, SNR=4); d) H2O2 in a red plastic

bottle (measured under daylight, SNR=5); e) acetaminophen behind a blue

fabric garment (measured under fluorescent background light, SNR=10)


Figure 5.5: Reference spectra of the respective components utilised for Set

A and Set B

Figure 5.6: Setup of SORS and alignment of the sample concealed in a

container

Figure 5.7: Preprocessing techniques performed on spectra obtained from

Set A

Figure 5.8: Eigenvector plot for PCA analysis

Figure 5.9: PCA scores plot utilising a) PC1 and PC2, b) PC1 and PC3

Figure 5.10: Loadings plots for PC1, PC2 and PC3

Figure 5.11: Cross validation results for (a) set A and (b) set B

Figure 5.12: PLS regression model for the quantitative determination of (a)

acetaminophen and (b) phenylephrine

Figure 5.13: Loadings of LV1 and LV2 for a) Set A and b) Set B

Figure 5.14: SORS analysis of ammonium nitrate in a white HDPE

container


obtained at different offsets for ammonium nitrate concealed in a white

HDPE container

Figure 5.16: Signal intensity ratio as a function of spatial offsets for the

SORS analysis of ammonium nitrate concealed in a white HDPE container

Figure 5.17: Signal-to-noise ratio as a function of spatial offsets for the

SORS analysis of ammonium nitrate concealed in a white HDPE container

Figure 5.18: SORS analysis of ammonium nitrate in a yellow polystyrene

container

Figure 5.19: Signal intensity ratio as a function of spatial offsets for the

SORS analysis of ammonium nitrate concealed in a yellow polystyrene

container

Figure 5.20: Signal-to-noise ratio as a function of spatial offsets for the

SORS analysis of ammonium nitrate concealed in a yellow polystyrene

container


obtained at different offsets for ammonium nitrate concealed in a yellow

polystyrene container


Figure 5.22: SORS Spectrum of ammonium nitrate concealed in a yellow

polystyrene container detected from 8 metres. A scaled subtraction

between spectra obtained at a zero offset and a 15mm offset was carried

out.

Figure 5.23: SORS analysis of aspirin in a white HDPE container

Figure 5.24: SORS analysis of 2,2-thiodiethanol in a white HDPE

container

Figure 5.25: SORS analysis of GBL in a white HDPE container

Figure 5.26: SORS analysis of hydrogen peroxide in a white HDPE

container

Figure 5.27: SORS analysis of 2,4-DNT in a white HDPE container

Figure 5.28: SORS analysis of nitromethane in a white HDPE container

Figure 5.29: SORS analysis of ammonium nitrate in a white HDPE

container

Figure 6.1: Effect of spatial offsets on the temporal profile of resulting

Raman photons

Figure 6.2: TR-SORS analysis of ammonium nitrate in a white HDPE

container at a 5mm spatial offset





Figure 6.5: TR-SORS analysis of ammonium nitrate in a white container at

a 20mm spatial offset



Figure 6.7: Signal intensity ratio for the TR-SORS analysis of ammonium

nitrate in a white HDPE container

Figure 6.8: Signal-to-noise ratio for the TR-SORS analysis of ammonium

nitrate in a white HDPE container

Figure 6.9: TR-SORS analysis of ammonium nitrate in a yellow

polystyrene container at a 10mm spatial offset










Figure 6.14: Signal intensity ratio for the TR-SORS analysis of ammonium

nitrate in a yellow polystyrene container

Figure 6.15: Signal-to-noise ratio for the TR-SORS analysis of ammonium

nitrate in a yellow polystyrene container

Figure 6.16: TR-SORS Spectrum of ammonium nitrate concealed in a

yellow polystyrene container detected from 8 metres. The measurement

was carried out at a spatial offset of 15mm and a gate delay of 86ns

Figure 6.17: TR-SORS analysis of aspirin in a white HDPE container at a

15mm spatial offset

Figure 6.18: TR-SORS analysis of 2,2-thiodiethanol in a white HDPE


Figure 6.19: TR-SORS analysis of GBL in a white HDPE container at a

15mm spatial offset

Figure 6.20: TR-SORS analysis of hydrogen peroxide in a white HDPE


Figure 6.21: TR-SORS spectra of (a) 2,4-DNT, (b) ammonium nitrate and

(c) nitromethane concealed in a white HDPE container

Figure 6.22: TR-SORS spectra of a) ammonium nitrate, (b) nitromethane

and (c) hydrogen peroxide in different non-coloured containers

Figure 6.23: TR-SORS spectra of (a) ammonium nitrate (b) ammonium

Nitrate (c) 2,4-DNT and (d) hydrogen peroxide in different coloured

containers


List of Tables

Table 5.1: Compositions of Set A and Set B

Table 5.2: Specifications of mixtures allocated to calibration and

prediction sets


List of Abbreviations

CW: Continuous wave

HDPE: High density polyethylene

PCA: Principal component analysis

PLS: Partial least squares

SORS: Spatially Offset Raman Spectroscopy

TRRS: Time-Resolved Raman Spectroscopy

TR-SORS: Time-Resolved Spatially-Offset Raman Spectroscopy

SNR: Signal-to-noise ratio


Statement of Original Authorship

The work contained in this thesis has not been previously submitted to meet

requirements for an award at this or any other higher education institution. To the

best of my knowledge and belief, the thesis contains no material previously

published or written by another person except where due reference is made.

Signature: _________________________

Date: _________________________


Acknowledgements

I am deeply grateful to the following individuals who have played a significant

role in pushing me forward throughout this research.

My mom for her unconditional love and continual motivational support

and encouragement that has always lifted me up at my down times, as well

as the support of my Dad and sister.

My principal supervisor, Dr Emad Kiriakous, for giving me the

opportunity to pursue this research in which I have learnt and experienced

much from. I appreciate his guidance as well as his continual support

throughout my entire research term.

Professor Peter Fredericks for the useful discussions and critical opinions

of the research data which led to the refinement and improvements of the

subsequent studies.

Dr Helen Panayiotou for having confidence in my capabilities and

encouraging me to pursue such an endeavour Dr Biju Cletus who has

played a vital role in my education of the physical concepts behind photon

migration and Raman spectroscopy. I am also grateful for his patience in

bearing with my inquisitive nature throughout our experiments.

Dr William Olds for training me on the use of the equipment as well as for

the useful discussions on the data procured.

Dr Mark Selby for imparting his knowledge of Chemometrics and his

guidance in experiments dealing with Chemometrics

Nick Ryan for assisting us in soliciting the necessary controlled items as

well as the procuring of the necessary space to conduct stand-off detection

analysis.

QUT librarians for their highly efficient document delivery system which

significantly aided in providing the necessary journal articles.

My friends; in particular, Seah Yueh Chinn, Dayalan Karpaya, Nathalie

Seah and Cassandra Seah for their consistent encouragement, care and

concern throughout my time in Australia which I deeply appreciate


Chapter 1: Introduction

This chapter outlines the background, scope of research, specific objectives as

well as a detailed outline of the subsequent chapters of this thesis.

1.1 BACKGROUND

Forensic & homeland security investigators as well as first responders

encounter concealed substances in various situations ranging from illicit drugs,

counterfeit medication to suspicious items that may contain potentially harmful

chemical substances such as explosive and chemical warfare substances. Existing

instrumental techniques utilised by investigators require the collection and

preparation of samples for the instrumental analysis as well as the physical

introduction of the sample to an analytical platform. Any time an instrument comes

into contact with the sample, it must either be disposed off in a controlled manner or

thoroughly decontaminated. Such techniques are complex, time consuming, and

potentially risky depending on the identity of the concealed substance. In many

instances, these instruments provide false positive results due to the lack of

specificity and the limited tolerance to environmental factors offered by these

techniques [1].

Raman spectroscopy is a spectroscopic technique of high chemical specificity

and tolerance towards environmental factors that may affect the analysis. Recently, a

new field known as Deep Raman Spectroscopy emerged for the detection of deep

layers within a diffusely scattering sample [2]. The techniques within this field have

demonstrated tremendous potential in the depth profiling of concealed substances.

However, since this research is at its adolescence, the available areas for further

investigation are plenty. In an effort to provide a better understanding of the existing

techniques within the field, this thesis aims to delve further into the individual

techniques and provide a better understanding of the concepts involved while

demonstrating and extending the capabilities of these techniques.


1.2 SCOPE

The scope of this research is aimed at three main techniques within Deep

Raman Spectroscopy; namely, Time-resolved Raman spectroscopy (TRRS),

Spatially-Offset Raman Spectroscopy (SORS) and Time-Resolved Spatially-Offset

Raman Spectroscopy. This study is focused on understanding the depth profiling

efficiency of these techniques relative to each other as well to extend the capabilities

of these techniques for the sole purpose of identifying concealed substances of

forensic interest

.

1.3 OBJECTIVES

The main objective of this dissertation is to extend the capabilities and to study

the efficiency of the three techniques within Deep Raman spectroscopy. Specific

aims include the following:

1. Develop a nanosecond-scale spectrometer for time-resolved Raman

spectroscopy (TRRS) and to test it at working distances of up to 15m.

2. Extend spatially-offset Raman spectroscopy (SORS) to the stand-off

analysis of concealed substances at working distances up to 15 m.

3. Investigate SORS for the qualitative and semi-quantitative analyses of

concealed substances with the aid of multivariate statistical treatments of

the spectral data.

4. Apply the developed nanosecond-scale spectrometer to time-resolved

spatially-offset Raman spectroscopy (TR-SORS) for the detection of

concealed substances in coloured and non-coloured packaging at working

distances of up to 15m

5. Make critical comparisons on the efficiency of the three techniques

relative to each other based on the degree of suppression of the Raman

signal arising from the surface layer as well as the resulting signal to noise

ratio of the spectra.

6. Utilise Deep Raman spectroscopic techniques for the analysis of concealed

samples that range from pharmaceutical ingredients to explosive

precursors to chemical warfare precursors.


1.4 THESIS OUTLINE

As outlined in the scope of research, this dissertation aims to focus on three

main techniques within Deep Raman Spectroscopy. As such, each chapter is

dedicated to a specific technique in Deep Raman spectroscopy. This is done to

provide a comprehensive guide on the concepts involved within each technique as

well as to present the results of the specific studies that this research has pursued.

The techniques are presented in the chronological order of their conception. Each

chapter begins with a literature review to introduce and inform the reader of the

concept of the technique as well as the research that has been conducted to date. The

following outlines the specific details of the subsequent chapters:

Chapter 2: This chapter provides a generic literature review of the type of

concealed substances that are commonly encountered within forensics and

homeland security, as well as a brief overview of the current techniques

utilised for the detection of such concealed substances. A brief review is

then presented on the Raman effect as well as the main concepts within

Deep Raman spectroscopy.

Chapter 3: An experimental design is provided which informs the reader of

the specific parameters, instrumentation and samples that are utilised in

this study.

Chapter 4: Time-Resolved Raman Spectroscopy (TRRS) is introduced

along with its concept. Stand-off TRRS is attempted and its efficiency is

determined.

Chapter 5: Spatially-Offset Raman Spectroscopy (SORS) is introduced

along with its concept. Continuous wave (CW) SORS is demonstrated on

white and coloured packaging along with a feasibility study of applying

chemometric techniques for the semi-quantitative prediction of target

analytes that are concealed in opaque packaging. Stand-off SORS

configuration is demonstrated. The efficiency of SORS is determined and

compared to that of TRRS.

Chapter 6: Time-Resolved Spatially-Offset Raman Spectroscopy (TR-

SORS) is introduced along with its concept. Close-range and stand-off TR-

SORS detection of substances concealed in coloured and non-coloured


packaging is attempted. The efficiency of TR-SORS is determined and

critical comparisons are made relative to TRRS and SORS.

Chapter 7: Chapter 7 summarises the findings throughout this dissertation

and makes key recommendations on areas that warrant further research.

‎Chapter 2: Literature Review 19

Chapter 2: Literature Review

2.1 INTRODUCTION

A wide range of samples are commonly encountered by forensic and homeland

security investigators. Every sample presents a unique challenge in terms of the

sampling techniques as well as the type of qualitative/quantitative analysis to be

adopted. Substances that are concealed pose a higher degree of challenge altogether.

A concealed substance refers to any substance that is packaged or wrapped within

another material such that the visibility of the substance in question is obscured or

entirely hidden.

A concealed sample may pose varying degrees of risk to the investigator

handling it. Common substances that are of key interest to the forensic and homeland

security investigations include explosive substances, chemical warfare agents

(CWA), illicit drugs and counterfeit pharmaceutical products. When dealing with an

unknown packaged item, the content could be any of the above which is the reason

why safety is warranted when dealing with a concealed item in such investigations.

Efforts behind homeland security and counter-terrorism have been directed

towards the prevention of attacks on a nation by any individual or group with a

nefarious intent. The importance of homeland security has been significantly

reiterated since the September 11th

attacks in 2001, that led to the demise of

thousands of victims, as well as the London bombings in 2005 [3, 4]. Similar attacks

in the past have involved the use of concealed explosives and chemical warfare

agents [5]. Rapid and accurate identification of such substances is required in order

to diffuse potentially hazardous situations. It is for this reason that the extent of

prevention is highly dependent on the availability of on-site detection techniques. As

such, it is this concern that beckons for an effective bulk detection technique that can

non-invasively detect potentially hazardous concealed substances. This review looks

into the challenges encountered by such concealed substance which is the main

motivation behind this research as well as the existing detection techniques utilised

for the identification of concealed substances.

Chapter 4: Literature Review 20

2.2 EXPLOSIVES

Explosions are associated with the generation of a large amount of matter and

heat, as a result of a rapid decomposition reaction, which exerts a voluminous

amount of pressure instantaneously that leads to the destruction and damage of

entities within the blast-radius of the explosive [6]. The explosive power is

dependent on the decomposition rates of the respective explosive component being

utilised.‎Rates‎lower‎than‎the‎speed‎of‎sound‎result‎in‎a‎‘deflagration’‎process‎which‎

is characterized by a subsonic combustion. Such explosives are termed as ‘low

explosives’. ‘High explosives’ exhibit decomposition rates higher than the speed of

sound‎result‎in‎a‎‘detonation’‎which‎is‎characterized‎by‎an‎induction‎of‎a‎supersonic‎

shock wave [7]. Explosives may be assembled in the form of an explosive train

which consist of a detonator, booster and a main charge [8].

2.2.1 Need for Selectivity

The destructive power of explosives has been put into use through numerous

military and commercial applications [9]. However, there has been a growing trend

in the use of explosives in terrorist-related activities [5]. One of the main reasons for

this is attributed to the ease of procuring instructional guides which are widely

circulated on the internet as well as the necessary ingredients within such

formulations that are available to the layperson [1]. Two main components are

required for an explosion; a fuel source and an oxidant. Using these components, an

Improvised Explosive Device (IED) [10] is constructed with ease using concoctions

that, though inexpensive, exert a tremendous degree of explosive force. Such

concoctions that have been used in the past and are among the increasingly popular

choices of insurgent groups include ammonium nitrate/fuel oil (ANFO) mixtures,

sodium chlorate-, nitrobenzene- as well as peroxide-based mixtures such as

triacetone triperoxide (TATP) and hexamethylene triperoxide diamine (HMTD) [11-

13]. Such explosives comprise of nitrogen-containing as well as non-nitrogen-

containing compounds. As such, selectivity is required to detect both types of

explosives as well as to distinguish one nitrogen-containing explosive from another.


2.2.2 Safety and Distance

IEDs have been encountered in war-torn areas, densely populated public areas,

airports as well as clandestine laboratories [13, 14]. Cases involving IEDs are

tremendously challenging, as the types of devices that are encountered are

unpredictable in terms of the manner in which it is constructed, the ingredients and

the amount of which being incoproporated, the type of detonation mechanism

(electric/mechanical) being utilised, the manner in which it is concealed and the

location where it may be positioned [1, 15].

This information aids investigators in understanding the possible extent of

damage that may take place and in strategising the appropriate actions to dispose,

detonate or diffuse the device. Knowledge of the identity and mass of the explosives

provides information of the estimated blast radius. However, explosives confined in a

container exert a higher degree of lethal force even if a low explosive is in use while

additionally causing more damage due to the container material acting as shrapnel.

This fact reiterates the need for a technique to non-invasively identify the content of

a suspicious package. Due to its large availability, plastic containers are often use as

the choice of concealment [16].

First responders within the vicinity of an IED are placed at high risks of

endangering their lives especially when secondary device(s) are in place. Current

crime scene examination protocols require the evacuation of the area to stay clear of

the hot and warm zone regardless of post- or pre-blast scenarios [17]. Secondary

devices, aimed at first responders, have been encountered in the past in the attacks at

Atlanta, USA in 1997, the foiled attacks at Columbine high school, USA in 1999 and

most recently at Yala, Thailand in 2012 [18-20]. IEDs have also been utilised as

booby traps installed in clandestine laboratories [21].

Security regulations at the airports have significantly tightened since the foiled

bombing plot of August 2006 which involved the smuggling of various components

of an IED through the security screening area, whilst disguised as inconspicuous

items, with the intent of assembling a peroxide-based IED on the plane [22]. This

event led to the strict regulations against the possession of liquids in hand-carry

luggage as well as the increasing use of detection techniques to screen passengers for

the potential presence of explosives or their precursors.


These events indicated the need for a detection technique that has the

versatility of detecting substances from close range to stand-off working distances.

Additionally, due to the preferential use of plastic packaging materials, the detection

technique should cater to the identification of the concealed substance despite the

presence of a barrier.

2.3 CHEMICAL WARFARE AGENTS (CWA)

Tracing its military roots to 1915, chemical warfare agents (CWA) have been

used for the main intention of directly or indirectly harming and/or taking the lives of

soldiers,‎thereby‎placing‎a‎nation’s‎opponents‎at‎a‎significant‎disadvantage‎during‎a‎

battle [23]. Despite efforts to eradicate the further production of CWA

internationally, CWA are still being manufactured in clandestine laboratories and

used as an effective form of attack by terrorists [24].

CWA and explosives share several similarities in the manner in which they are

smuggled, administered for sabotage as well as the type of places that are targeted. A

major incident that took place at a subway in Tokyo, Japan in 1995 exemplified the

simplicity in administering CWA to the commuters without their knowledge [25].

The incident involved the use of sarin concealed in perforated plastic bags which led

to massive injuries and deaths. Attempts to dispose the bags also led to the demise of

two of the first responders attending to the scene which further reiterates the need for

stand-off detection techniques to ensure the safety of the investigators involved.

2.4 ILLICIT DRUGS AND COUNTERFEIT PHARMACEUTICAL

PRODUCTS

An illicit drug is a naturally occurring or synthetic substance that induces

psychological stimulation and is consumed recreationally [6]. The continual use of

illicit drugs leads to addiction, detriment to‎one’s‎health‎[26] as well as drug-related

crimes [27]. Efforts in eradicating the distribution and consumption of illicit drugs

have been futile due to the numerous underground networks of drug manufacture,

clandestine laboratories as well as the ongoing search for alternative stimulants that

provide similar effects as existing illicit drugs in order to replace existing illicit drugs


that have been legally restricted or banned [28]. Tight security measures have been

established at airports in an effort to prevent the circulation of illicit drugs. However,

new ways of smuggling illicit substances have been attempted in many cases which

include the dissolution of such substances in beverages [29, 30] as well as concealing

them in various ways [31].

The dangers of counterfeit pharmaceutical products is significant as it deals

with‎ the‎ consumers’ health [32]. Consequences can be dire if such pharmaceutical

products are intended for the cure of a disease such as anti-malarial drugs [33, 34].

Visual discrimination of a legitimate product from a counterfeit one may be

challenging [35]. Conventional methods of determining the legitimacy of a product

involves the application of invasive sample preparation techniques to the suspected

packaging which completely renders the product unusable.

2.5 EXISTING BULK DETECTION TECHNIQUES

Instrumental techniques have given an edge to the scientific community mainly

due to their higher output efficiency and sensitivity in comparison to wet-laboratory

techniques. Detection techniques are categorised into bulk detection and trace

detection schemes. Trace detection techniques exhibit high sensitivity and selectivity

to the substances of interest which facilitate the detection of substances in trace

amounts [36]. Techniques within this domain such as ion mobility spectrometer

(IMS) and gas chromatography (GC) utilise vapours emitted by the substances or

particles that have been deposited on surfaces within the vicinity of a sample of

interest [37, 38]. This requires invasive sampling techniques to draw a sufficient

amount of vapour or particles which requires a pre-concentrator or swabbing

techniques [39]. In situations where a concealed substances is encountered, the

vapour pressure of the content is significantly suppressed which complicates the

utility of trace detection techniques [40, 41]. Additionally, invasive sampling

techniques require the analysts to be in close contact with the sample which may be

risky when dealing with a potentially harmful concealed substance.

Bulk detection facilitates the detection of substances and has the sensitivity that

caters to the detection of substances present in the amount of grams and above. The

main aim of bulk detection techniques is to facilitate the detection of a concealed


substance in question as a first form of defence against illicit materials of various

forms [42]. Based on the challenges posed by concealed substances that have been

highlighted in the preceding sections, a suitable technique should have the capability

to perform the required detection non-invasively at various working distances with

high accuracy and specificity. The analytical platform should be portable and tolerant

to real life environmental conditions. Existing bulk detection methods utilise nuclear

techniques, X-ray techniques and laser-based techniques. [43-46].

2.5.1 Nuclear techniques

Neutron-based Techniques

These‎techniques‎are‎based‎on‎the‎‘neutron-in gamma-out’‎concept‎where‎upon‎

irradiation of a nucleus with a stream of neutrons, the absorption of a neutron by the

nucleus takes place which results in the emission of gamma rays [47]. The resulting

energy of the gamma rays is characteristic of the specific nuclei being analysed

which facilitates the identification of a substance. Due to the low neutron cross

section exhibited by most dielectric materials in general, neutron analysis exhibits a

high penetrating capability which facilitates the depth profiling of concealed

samples, more so than X-ray techniques [48]. Techniques utilising this concept

include thermal neutron analysis (TNA) [49], fast neutron analysis (FNA) [50],

pulsed fast neutron analysis (PFNA) [51] and pulsed fast thermal neutron analysis

(PTFNA) [52] where their applications extend to the detection of explosive

substances and illicit drugs. However, these techniques are not safe for the screening

of people [47]. Additionally, the inability to distinguish between nitrogen containing

samples limits the selectivity of these techniques while the limited spatial resolution

leads to low signal-to-noise ratio [1]. There have also been no publications indicating

its application to stand-off working distances.

Non-neutron based techniques

In contrast to neutron based techniques, these techniques involve the probing of

a nucleus with particles other than neutrons [48]. Such techniques include nuclear

magnetic resonance (NMR) [53] and nuclear quadrupole resonance (NQR) [54].


These techniques are also capable of probing through packaging materials to identify

the contents. However, in the case of NMR, an external magnetic field is required.

This requires the need to physically position a sample in an appropriate position

which entails invasive procedures such as investigators coming into contact with a

potentially hazardous substance [1]. Additionally, detection via NQR is restricted to

samples in the form of crystalline solids and is not capable of detecting non-nitrogen

based explosives [48]. Furthermore, both techniques are bulky and heavy, thus

limiting their portability.

2.5.2 X-ray Based Detection Techniques

X-ray based detection techniques are commonly used in numerous security

settings at key civic locations. The high penetration capability of X-rays facilitates

the retrieval of high resolution imaging, effective nuclear charge (Zeff) as well as the

density of the concealed items while being safe enough for the screening of humans

[1]. Its operation is based on the absorption of energy by the sample, thereby

attenuating the incident X-ray energy. Samples that are of higher density tend to

absorb more energy, resulting in darker images. X-ray techniques are generally safer

than nuclear techniques and less expensive [55]. However, they are unable to

distinguish peroxide-based explosives such as TATP and HMTD since their densities

fall within the average density of a wide range of common organic substances [1].

Existing X-ray techniques that are commonly used include single energy imaging

systems, dual – and multi-energy imaging systems, backscatter imaging systems, X-

ray diffraction and computer tomography [55]. Single energy imaging systems

require transmission geometry in order for the technique to operate. This in itself is a

limitation since it would require access to both sides of a sample. In a situation where

a suspicious packaging is discovered to be positioned at the corner of a room, the

application of such a technique would be restricted. Dual-/multi-energy imaging

systems were developed as an improvement to single-imaging systems. It is capable

of distinguishing densities to intrinsic or extrinsic properties of the concealed sample

as opposed to single-imaging systems [56].

However, for all X-ray techniques, the determination of the density is not

sufficient‎in‎contributing‎to‎the‎technique’s‎ability‎to‎be‎selective which is indicated


by the degree of false positives demonstrated by the techniques [57]. In the event of

false positives, operators are obligated to conduct a thorough examination of the

contents which is time consuming an infeasible in places where timing is crucial such

as the airport. Stand-off x-ray analysis at 10 metres has been attempted but

increasing the working distance is detrimental to the sensitivity of such techniques.

Images retrieved from utilising these techniques only aid in visually locating a

potentially dangerous substance such as wires but are limited in terms of identifying

the concealed substance. Additionally, X-ray techniques are incapable of providing

any quantitative information.

2.5.3 Laser Based Techniques

In contrast to X-ray and nuclear based techniques, laser-based techniques are

capable of stand-off detection modes due to the diffraction-limited feature exhibited

by the use of a laser [58]. Terahertz spectroscopy and Raman spectroscopy, in

particular, have demonstrated significant potential for the use of bulk detection of

substances.

Terahertz Spectroscopy

Terahertz radiation falls within the region of 0.1 - 10 THz in the

electromagnetic spectrum. Since it lies between the infrared and microwave regions

of the electromagnetic spectrum, Terahertz spectroscopy is able to provide

information regarding the vibrational and rotational modes of the molecules being

studied [59]. As such, when a sample is probed with terahertz radiation and

undergoes its respective transitions, the resulting Terahertz radiation emitted is

characteristic of the sample. Terahertz spectroscopy has been demonstrated to

provide imaging and spectral information on concealed explosives and drugs [60].

Due to the transmission properties of terahertz radiation within a host of dielectric

materials, it is able to probe explosive substances concealed within containers [61],

layers of fabric [62] in envelopes [63] and other non-metallic vessels [64, 65].

Additionally, its safety in terms of human exposure facilitates the application for on-

site detection [66]. It can also be utilised for the detection of phase changes within

explosives [67] while providing high signal-to-noise ratio [68]. Its molecular


specificity accounts for its high selectivity and its ability to perform stand-off

detection has been demonstrated with much success [69, 70]. However, for some

explosive substances such as ammonium nitrate and sodium perchlorate,

identification is complicated by a lack of distinct spectral features [71]. Additionally,

despite the capability to perform stand-off detection for, the resulting spectra are

subjected to attenuation due to environmental factors such as atmospheric

absorbance as well as humidity [72-74]. Although Terahertz spectroscopy presents

itself to be a potential technique for the stand-off and close-range detection in

homeland security, much work is required to develop its ability to become field-

deployable.

Raman Spectroscopy

The underlying concept of Raman spectroscopy is based on the inelastic

scattering of photons [75-77]. Scattering of photons generally occurs upon the

excitation of a sample with monochromatic light. The molecules of a sample are

excited from its original vibrational state to a short-lived virtual state. Almost

instantaneously, the molecules relax whilst emitting a photon. Depending on the

exact state at which the molecule returns to, the scattered photons may be categorised

as elastic scattering or inelastic scattering.

Elastic (or Rayleigh) scattering is the dominant process in which the scattered

photon possesses the same wavelength as the incident photon (Figure 2.1). Such

phenomenon occurs when the molecules of a sample return to their original ground

state, thus experiencing no change in energy, Inelastic scattering corresponds to

instances where a molecule relaxes to a relatively higher or lower state,

Consequently, the scattered photon posses a shorter or longer wavelength

respectively than the incident photon.


Figure 2.1: Scattering phenomena as a result of monochromatic excitation of a sample.

Raman scattering can be further characterised as Stokes and anti-Stokes

scattering (Figure 2.1). Scattered photons that posses a relatively longer wavelength

are characterised as having undergone stokes scattering where, upon excitation, the

molecule is promoted to a higher vibrational state by utilising the energy from the

incident photons. Anti-stokes scattering involves the excitation of molecules that are

initially present at higher vibrational states. Upon excitation, the molecules tend to

relax to a lower vibrational state, thus emitting photons of relatively shorter

wavelengths. However, the number of molecules present at higher vibrational states

is proportional to the existing temperature. At room temperature, a significantly

lower proportion of molecules is present at higher vibrational states. As such, Stokes

scattering is the preferred mode of analysis in Raman spectroscopy.

In comparison to elastic scattering, an inherent limitation within Raman

scatttering is that one in every 106-10

8 scattered photons are Raman photons,

resulting in a significantly weak Raman signal [77]. Hence, a significant challenge in

Raman spectroscopy involves the filtering of the miniscule number of Raman

photons. Lasers are commonly utilised as an excitation source in Raman

spectroscopy due to its ability to provide a high intensity diffraction limited beam

which enhances the number of Raman scattered photons [58]. Additionally, the

Incident Radiation

Rayleigh

Raman Shift (cm-1)

Inte

nsi

ty

Sample

Vibrational Energy States

Virtual Energy States


number of Raman scattering intensity is inversely proportional to the fourth power of

the laser‎wavelength‎(1/λ4) The use of wavelengths within the ultraviolet (UV) region

has been demonstrated to increase the signal by up to 106 times. This has been

demonstrated by the spectral profile obtained from explosives within the UV region

[78]. However, choosing an appropriate wavelength is a compromise between a

desirable Raman scattering intensity and the intensity of fluorescence which may

overwhelm a spectral profile [79, 80]. The extent to which a good Raman signal is

obtained is also dependent on the degree of polarisability of the molecule in question

during an excitation [77]. Incidentally, molecules that exhibit low polarity result in

relatively stronger Raman signals as opposed to samples with high polarity.

However, this has proven to be advantageous in some situations. For instance, water

exhibits high polarity thus resulting in significantly weak Raman signals. As such,

samples of interest may also be detected even though they may be dissolved in water

[81].

Raman spectroscopy is venerated for a host of capabilities which include its

high chemical specificity as well as its tolerance to environmental conditions such as

humidity as opposed to Terahertz spectroscopy [82]. Its high chemical specificity

facilitates the identification and discrimination of various chemicals despite their

similar molecular characteristics [83, 84]. Additionally, it also facilitates the efficient

application of chemometric techniques in order to perform qualitative and

quantitative analyses on the resulting multivariate spectral data [85]. The adaptability

of Raman spectroscopy to the field of forensic science has been demonstrated by the

numerous applications which have been comprehensively documented in a recent

publication [86]. This includes its efficient applications for explosive precursors [87-

89], CWAs [90-93], as well as the qualitative and quantitative analysis of illicit drugs

and counterfeit pharmaceutical products [89, 94-98].

The potential of a stand-off Raman spectroscopic system to detect substances

from a significant working distance was first proposed in the 1960s [99]. In contrast

to conventional Raman spectroscopy, however, stand-off Raman spectroscopy

requires a laser source with sufficient power to transmit at significant distances as

well as a detection system such as a telescope to efficiently collect the resulting

Raman photons from that distance which did not have the required efficiency at the

time of its conception [99]. The development of such enhanced illumination and


detection systems with time [100-102] has led to the emergence of efficient

configurations that are capable of stand-off detection of explosives, among others,

for up to 470m under varying weather conditions [7, 103, 104]. The significant

limitation of this system is that it is not applicable to the detection of substances

concealed in an opaque packaging material [99].

This is due to the inherent limitation of conventional Raman spectroscopic

configurations which utilise a backscattering geometry. A backscattering geometry is

achieved by aligning the collection optics in such a way that the backscattered

photons from the excited spot are collected. However, this also results in spectra that

are always overwhelmed with fluorescence and Raman photons from the surface

layer. This is the main obstacle when attempting to utilise conventional Raman

spectroscopy for the detection of a concealed substance.

Kim et al proposed utilising a circular excitation beam which covers a larger

illumination area of 28.3mm2 in order to provide a representative and reproducible

spectrum [105]. Using the WAI setup, quantitative results were obtained in the non-

invasive and non-destructive analyses of active pharmaceutical ingredients in tablets

[105], capsules [106], liquids contained in clear plastic bottles [107] as well as

suspensions in clear plastic bottles [108]. The presented spectra indicated the depth

resolution capability of the WAI configuration and its ability to probe through such

media to detect the concealed substance. Whilst covering a larger illumination area,

the wide circular excitation beam additionally excites positions of the surface offset

from the point of collection which allows partial discrimination of the subsurface

layer along with the dominant surface layer. Despite its ability to provide

reproducible results with minimal error and the partial retrieval of the deeper layer,

the spectra were still overwhelmed with spectral features of the surface layer. This is

indicative in a recent analysis in the detection of hydrogen peroxide contained in a

red plastic bottle [109]. For this reason, WAI is best suited in cases where the

spectral profile of the container and the content are known. Such an application

would include process analytical technology (PAT) where quality control is of the

main concern.

Among the existing techniques utilised for the bulk detection of samples of

interest, conventional Raman spectroscopy has been demonstrated to be highly,

tolerant to environmental conditions and capable of stand-off detection for the


qualitative and quantitative analysis of samples. However, it is limited in terms of the

depth profiling of concealed substances. Recently, Matousek et al introduced a new

domain known as Deep Raman Spectroscopy which comprises of techniques that

have shown potential for the detection of concealed substances [110-112]. In deep

Raman spectroscopy, the Raman spectra from the deeper layer (content) are recorded

while the Raman and fluorescence radiation arising from the surface layer

(packaging material) are suppressed by means of time or space resolution or a

combination of both.

2.6 DEEP RAMAN SPECTROSCOPY

2.6.1 Photon Migration in Diffusely Scattering Media

Diffusely scattering media are characterised by their opacity (or minimal

degree of transparency) which is a result of the densely populated particles within

such media. The propagation of photons through such a medium experiences

multiple scattering. As a result of the multiple scattering of photons, the medium

appears to be opaque and objects behind such media are non-discernable [113].

Photon migration in diffusely scattering media results in the diffusion of

photons upon impinging onto a diffusely scattering medium [114, 115]. The resulting

components of light are distinguished by the degree of scattering that they encounter

within a medium as a function of the total path length traversed by the incident

photons (Figure 2.2).

Ballistic components of light propagate through a medium with no deviation

from its original direction of propagation. Due to the subsequent scattering events the

photons slightly deviate from its original direction, though its directionality is still

forward biased. Such photons are labelled as the snake components of light. Diffused

components of light are photons that have traversed depths beyond its ballistic and

snake counterparts. As such, it experiences a significantly larger number of scattering

events that consequentially randomise its directionality as opposed to the ballistic

and snake components of light.


Figure 2.2: Photon propagation profile as a result of photon diffusion

It is the diffused components of light that is capable of providing depth

resolved information on a sample due to the depths traversed. Based on the longer

path traversed as well as its randomised directionality, Das et al indicated that the

diffused components can be discriminated based on temporal and spatial

distributions [114].

2.6.2 Existing Techniques in Deep Raman Spectroscopy

Application of this concept to Raman spectroscopy paved the way to the

development of Deep Raman Spectroscopy where depth profiling is achieved by

temporally and spatially resolving Raman photons originating from the deeper layer

of a sample from those generated from the surface layer which is applicable to the

detection of concealed samples. For example, a plastic container containing sugar is

akin to a two layered diffusely scattering sample in which the container mateiral is

the surface layer and sugar constitutes the deeper layer. Application of Deep Raman

spectroscopy thus facilitates the detection of the sugar concealed within the plastic

container via temporal and spatial resolution of the Raman photons from the deeper

layer. Figure 2.3 lists the existing variants within deep Raman spectroscopy based on

their respective utility of a temporal or spatial resolution. Specific details of the


techniques as well as research developments will be discussed at length in the

respective chapters.

Figure 2.3: Existing variants within Deep Raman Spectroscopy

Chapter 4: Experimental Design 34

Chapter 3: Experimental Design

The following chapter describes the instrumental configurations that were

adopted throughout the analysis as well as the samples utilised as the concealed

content. The concealed chemical substances in different packaging materials were

screened by Deep Raman Spectroscopic techniques. The experimental setup was

carried out with the aid of the physics department.

3.1 INSTRUMENTATION

The experimental design of SORS, TRRS, and TR-SORS instrumentation as

well as measurement parameters are detailed within this section.

3.1.1 Stand-off pulsed TRRS / SORS / TR-SORS

A schematic diagram of the instrumentation for stand-off detection by Deep

Raman spectroscopy is illustrated in figure 3.1. For excitation, a second harmonic

532nm Q-switched Nd:YAG pumped laser (Brilliant EaZy, Quantel, USA) with a

pulse length of 4ns and a pulse repetition rate of 10Hz was used. The 532nm laser

pulse was first collimated and expanded by a beam expander (HEBX-10-5X-532,

CVI Melles Griot, USA). The excitation beam at the surface of the sample was 2 cm

in diameter. The collection scheme consisted of a telescope whereby a catadioptric 8-

inch telescope (C8-XLT OTA, Celestron, USA) facilitates the collection of a large

number of photons from stand-off distances.

The collected returning photons propagate to the telescope through an 8-inch

front corrector window after reflection from the primary and secondary mirrors and

focused onto an output port. The focal point at the output port can be varied

accordingly by changing the primary mirrors with the help of the focussing knob.

The elastically-scattered photons are filtered through a 532nm long-pass filter

(Semrock U.S.A). A 2 inch lens (6 cm focal length) is used to focus the returning

photons onto a 900μm fibre bundle that consists of 19 individual fibres, each with a

core‎diameter‎of‎200μm.


Figure3.1: Schematic instrumental configuration of the stand-off deep Raman spectrometer

Raman photons propagating through the fibre bundle are transmitted to an

Acton SP2300 spectrograph (Princeton Instruments, USA). The spectrograph (0.3m

focal-length) was fitted with three different diffraction gratings for the dispersion of

the incoming photons. The photons were then detected by an intensified charged

coupled device (ICCD) camera (PIMAX-1024, Princeton Instruments, U.S.A). The

gated detections were carried out by triggering the ICCD camera with a Q-switch

output signal from the laser controller. The gate width of the ICCD was set to 4ns in

order to facilitate the detection of a higher population of the deeper layer Raman

photons. The resulting spectra were acquired on a software application (WinSpec,

Princeton Instruments).

An oblique geometry was adopted in this scheme whereby the laser was aimed

directly at the sample while the field of view of the telescope coincides with the laser

excitation point such that it collects the returning Raman photons at an oblique angle.

Utilising an oblique geometry ensures that the total laser power reaches the sample as

opposed to utilising a coaxial geometry [99]. This configuration was utilised for

stand-off detection by three deep Raman spectroscopy modes (spatially offset

Raman, time-resolved Raman and spatially offset time-resolved Raman

spectroscopy) at working distances of 3m, 8m and 15m as indicated in figure 3.2.

where sample positions are outlined in red.


Figure 3.2: Stand-off detection performed at (a) 3m, (b) 8m and (c) 15m


3.1.2 Continuous Wave (CW) SORS Detection at 6cm

A schematic diagram of the instrumental configuration is presented in figure

3.3. Backscattering collection geometry was adopted in this system. For laser

excitation, a 785nm diode laser (BRM-785; BWTek) operating at a power of

~450mW was used. The laser excitation beam was spectrally purified with a

bandpass filter (LD01-785/10-25; Semrock) in order to remove the residual

amplified spontaneous emission components. An axicon lens (Del Mar) was used to

control the shape of the illumination beam to either an annular (ring) illumination or

a spot illumination. To facilitate the switching from one shape to another, the axicon

was mounted on a 250mm cage-rail system (ThorLabs Inc.) such that by sliding the

axicon, the focal point of the beam is readjusted to either form a spot illumination or

a ring illumination. The diameters of the spot illumination as well as the ring-shaped

illumination were ~4mm and ~16 mm respectively. The offset provided by the ring

illumination was 8mm.

Raman photons were collimated using a 50mm diameter biconvex lens of

60mm focal length. As such, the sample was positioned 60 mm in front of the

collection system to coincide with the front objective lens of the collection system.

The elastically-scattered (Rayleigh) photons were suppressed by a 50 mm notch

filter. The collected Raman photons were then focused by a rear objective lens (focal

length of 60 mm) onto a 900 μm diameter optical fibre bundle consisting of 19 fibres

(each‎fibre‎has‎core‎diameter‎of‎200μm).‎An‎additional‎notch‎filter and a long-pass

filter were positioned just in front of the optical fibre bundle to further suppress any

residual Rayleigh photons.

Figure 3.3: Schematic diagram of CW inverse-SORS configuration


The other end of the fiber bundle was vertically stacked into a ~4mm strip and

aligned‎ to‎ the‎ entrance‎ slit‎ (200μm)‎ of‎ the‎ spectrograph‎ (SP2300;‎ Princeton‎

Instruments). The dispersed Raman photons were detected by a thermoelectrically

cooled (-70oC) 256 x 1024-pixel CCD camera (PIXIS 256, Princeton Instruments).

The 256 pixels were vertically binned and acquired on a pc (WinSpec, Princeton

Instruments) as a single spectrum. Background correction was performed using a

background spectrum acquired when the laser was switched off. All measurements

were conducted in the dark.

3.1.3 TR-SORS Detection at 6cm

A schematic diagram of the instrumentation for TR-SORS detection at close

range is provided in figure 3.4. The TR-SORS configuration was established by

modifying the CW SORS unit described in section 3.1.2. A 785nm NIR pulsed laser

source (VIBRANT Opotek Inc, USA) operating at an average power of 20mW was

used for excitaion. An axicon lens (Del Mar) was positioned in front of the laser

source to create an annular illumination of 14mm in diameter, thus providing a radial

offset of 7 mm. Samples were positioned at a distance of 6cm from the Raman

collection system. The photon collection scheme is akin to that described for CW

SORS. The detection was carried out using the ICCD detector reported earlier in

section 3.1.1. Spectral measurements were obtained at a gate delay of 76 ns. The

Raman spectra were acquired by using 100 pulses and 5 accumulations per

measurement. The resulting spectra acquired from coloured materials were baseline

corrected using a weighted least squares algorithm.

Figure 3.4: Schematic diagram of the TR-SORS instrumentation


3.2 CHEMICALS

The following section describes the chemical substances that were utilised

throughout the study. Reference Raman spectra of the chemical substances used in

this study are acquired by conventional Raman spectroscopy and provided along with

assignments of the characteristic vibrational mode(s). Each of the Raman

measurements reported in this study was repeated 6 times (n=6). Prior to each Raman

measurement, the containers utilised were thoroughly rinsed with ethanol, acetone

and distilled water to ensure that the surface of the used packaging was free of

contaminants.

2,2-thiodiethanol

2,2-thiodiethanol (≥99%)‎was‎procured‎from‎Sigma-Aldrich. It is a colourless

viscous liquid sample which, aside from its industrial usage, is a precursor for the

manufacture of blister agents utilised in chemical warfare.

Figure 3.5: Raman spectrum of 2,2-thiodiethanol

500 1000 1500

4

6

8

10

12

14

x 104

Raman Shift [cm-1

]

Inte

nsity [C

ou

nts

]

Wavenumber(cm-1)

VibrationalModes

640 S-C stretching

740

995 C-C stretching


2,4-dinitrotoluene (2,4-DNT )

2,4-DNT (≥99%)‎was‎ procured‎ from‎ Sigma-Aldrich. It is a yellow coloured

crystalline solid sample that is utilised as a precursor for the production of

trinitrotoluene (TNT) which is categorised as a high explosive.

Figure 3.6: Raman spectrum of 2,4-dinitrotoluene

Ammonium Nitrate

Ammonium nitrate (≥99%)‎was‎procured‎from‎Austratec‎Phytotech‎Labs.‎It‎is‎

a white crystalline solid that has a characteristic peak located at 1020cm-1

which is

attributed to the symmetric stretching of NO3-. Ammonium nitrate is an explosive

precursor which has been used in numerous occasions in the past as part of an ANFO

mixture in an improvised explosive device (IED) where it is commonly concealed

within containers that are easily procured by the layperson [14].

Figure 3.7: Raman spectrum of ammonium nitrate

500 1000 1500

0.5

1

1.5

2

2.5

3x 10

5

Raman Shift [cm-1

]

Inte

nsity [C

ou

nts

]

Wavenumber(cm-1)

VibrationalModes

710 NO3-

stretching1020

1400 NH4+

stretching1437


Aspirin

Aspirin or acetyl-salicylic acid (≥99.5%),‎procured‎from‎Ajax‎Finechem,‎ is a

white crystalline solid which is commonly utilised as an antipyretic, analgesic and an

anti-inflammatory medication.

Figure 3.8: Raman spectrum of aspirin

Gamma-butryolactone (GBL)

Gamma-butyrolactone (GBL) (≥99%),‎ procured‎ from‎ ISP (Australasia) Pty

Limited, is a colourless viscous liquid which is utilised as an industrial cleaning

agent. However, it is also a precursor to a notorious illicit drug known as gamma-

hydroxybutyric acid (GHB), commonly labelled as‎the‎‘date-rape‎drug’.‎

Figure 3.9: Raman spectrum of GBL

1000 1100 1200 1300 1400 1500 1600 1700

4

5

6

7

8

9

10

11

12

x 105

Raman Shift [cm-1

]

Inte

nsity [C

ou

nts

]

O

OH

O

O CH3

Wavenumber(cm-1)

VibrationalModes

1012C-H bending

1160

1295 O-H bending

1606 C-C stretching

1620 C-O stretching


30% v/v Hydrogen Peroxide

30%v/v aqueous hydrogen peroxide (H2O2), procured from Merck, is a

colourless liquid with strong oxidising properties which has been utilised in the

preparation of peroxide-based explosives.

Figure 3.10: Raman spectrum of hydrogen peroxide

Nitromethane

Nitromethane (≥98.5%),‎procured‎from‎Scharlau‎Chemie‎S.A.,‎ is a colourless

liquid that is conventionally utilised as fuel. However, it has been labelled as a high

explosive and is commonly combined with an oxidizer.

Figure 3.11: Raman spectrum of nitromethane

The samples were concealed in different packaging materials that included

fabric and a range of non-coloured as well as coloured containers made of high

density polyethylene (HDPE), polystyrene and polypropylene. The spectral profile of

the respective containers utilised for a specific study are provided in the respective

sections.

Chapter 4: Time-Resolved Raman Spectroscopy 43

Chapter 4: Time-Resolved Raman Spectroscopy

4.1 INTRODUCTION

Time-resolved Raman Spectroscopy (TRRS) was initially conceived as a

means to suppress the persistent problem of fluorescence which has the tendency to

overwhelm Raman signals. Fluorescence suppression via TRRS is achieved by the

temporal resolution of Raman photons that arrive at the detector earlier than

fluorescence [116-121]. This temporal discrimination was demonstrated by utilising

an impulsive excitation source and a gated detection system. Subsequent

improvements in instrumentation paved the way to a picosecond-scale system where

a picosecond-pulsed laser and Kerr gated detection system was utilised to suppress

fluorescence in homogenous films and this technique was demonstrated to be more

efficient in fluorescence suppression than Fourier-transform Raman (FT-Raman)

spectrometry [122].

Studies in photon migration and the resulting scattered components within

diffusely scattering media reported that a similar principle of temporal discrimination

of photons could be applied to resolve diffused photons arising from the deeper layer

of such a medium [114]. Application of these studies to the behaviour of Raman

scattered photons within diffusely scattering samples led to the realisation of the

differing arrival times (in a backscattering collection geometry) of Raman photons

arising from the surface layer and those arising from the deeper layer of a sample

[123, 124]. Matousek et al demonstrated that, in a two-layered diffusely scattering

sample, the Raman photons originating from the second layer could be detected

hundreds of picoseconds following an excitation by a 1ps laser pulse [111]. Utilising

a Kerr gated collection system as well as a picosecond-pulsed laser in a

backscattering geometry, TRRS was demonstrated to be an effective technique for

the interrogation of the deeper layer of a diffusely scattering sample whilst

suppressing fluorescence and Raman photons arising from the surface layer by

temporally detecting the Raman photons, through gate delays, arising from the

deeper layer with a resolution of 4ps [111, 125, 126].

In view of the cost and complexity of operating a Kerr gated system, an

intensified charged coupled device (ICCD) was utilised as an alternative gated


detection system by Ariese et al [127]. Despite the lower temporal discrimination

power of the ICCD relative to the Kerr gated system, it was capable of depth

profiling through diffusely scattering layers without rigid laser requirements that are

conventionally required when utilising a Kerr gated system [128]. However, when

the same configuration was utilised for the non-invasive detection of concealed

explosives in sheets of various polymer materials, the spectral features from the

surface layer were still apparent within the acquired spectra and a full suppression of

the surface layer was not achievable [129]. Data treatments involving the use of

scaled subtractions were suggested to obtain a spectral profile that is representative

of the deep layer. Additionally, the signal-to-noise ratio in the acquired TRRS

spectra is generally low. This is attributed in part to the use of a picosecond-scale

detection scheme with a significantly narrow detector gate width (~250ps) which

restricts the detection to a diminished number of Raman photons from the deeper

layer [129].

To date, there has been no further discussion or attempts made to improve the

signal-to-noise ratio of the Raman signals obtained in TRRS. Additionally, TRRS

has not been attempted on concealed samples from a stand-off distance which is of

significant value to homeland security applications in dealing with explosive

substances.

4.2 AIMS

- To utilise a nanosecond-scale system for a TRRS configuration

- To conduct stand-off detection of concealed samples at working distances of up

to 15m.

- To determine the degree of selectivity of stand-off TRRS towards the deeper

layer of a sample.

- To investigate the signal-to-noise ratio of the resultant TRRS spectra.


4.3 CONCEPT OF TRRS

To comprehend the effect of implementing gate delays in TRRS, one has to

consider the temporal characteristics of the respective Raman photons arising from a

diffusely scattering sample.

Sinfield et al illustrated the resulting Raman and fluorescence profile at

progressive stages of a laser pulse as it impinges onto a fluorescing non-diffusely

scattering neat sample [130]. The findings indicated that Raman photons can be

temporally resolved since it arrives at the detector earlier than fluorescence. Ariese et

al illustrated the temporal profiles of the resulting Raman photons originating from

the first and second layer of a two-layered diffusely scattering sample upon

excitation by a single laser pulse [127]. The emphasis was on the distinct delay

between the arrivals of both sets of Raman photons where the photons from the

surface layer tend to arrive at the detector earlier than those from the deeper layer.

However, the emergence of fluorescence was not considered.

In contrast to a sample that is not diffusely scattering, Raman photons

propagating through a diffusely scattering medium experiences a relatively larger

number of scattering events. As a result of these multiple collisions, Raman photons

tend to spend a longer time within a diffusely scattering sample before re-emerging

from the surface. Additionally, the total depths traversed by these photons include

the paths traversed from the point of illumination and back to the detector in a

backscattering geometry. Due to the longer time taken for Raman photons to arrive at

the detector following a pulsed laser excitation, the temporal profiles of the Raman

photons are significantly broadened [110]. Furthermore, Raman photons arising from

the deeper layers exhibit a relatively broader temporal profile than those arising from

the surface layer due to the relatively larger depths traversed within the medium as

illustrated in figure 4.1a-b [123]. As a result of this relatively larger temporal

broadening exhibited by the deeper layer Raman photons, the intensity of deeper

layer temporal profile is lower than that of the surface layer.

Fluorescence occurs within the order of 1ns second following the excitation of

the sample. Its lifetime may range from 1-50ns depending on the type of fluorophore

present as well as the wavelength of the utilised excitation source [128]. An arbitrary

fluorescence lifetime is utilised to present the concept of TRRS (Figure 4.1c).


Figure 4.1: Temporal profile of Raman photons and fluorescence arising from a two-layered diffusely

scattering medium

Figure 4.2 illustrates the resulting temporal profiles of the developing Raman

photons from the surface and deeper layers at different stages of an excitation laser

pulse (front, bulk and tail of the pulse) as it impinges onto a sample. The detector

gate is temporally shifted to a specific delay in which the surface layer photons may

be greatly suppressed (avoided) while maintaining the detection of the deep layer

Raman photons [131].


Figure 4.2: Temporal profiles of Raman photons from the surface and deeper layers of a sample at

different stages of an impinging laser pulse

T1 – T2: T1 to T2 indicates the developing surface layer Raman photons

upon impingement of the leading edge of a 4ns laser pulse

onto the surface layer of a sample. Some photons undergo

scattering on the surface while others are in the process of

propagating further into the sample.

T2 – T3: Between T2 to T3, incident photons from subsequent segments

of the 4ns laser pulse continue to excite the sample. The

number of Raman photons from the surface layer is increasing.

Additionally, fluorescence begins to emerge at low levels.

Furthermore, the excitation photons that sneak into the bulk of

a sample generate Raman photons from the deep layers of the

sample.

T3 – T4: Towards the leading edge of the pulse, Raman scattering is

still occurring. At this time, fluorescence from the surface

layer occurs at high levels. Meanwhile, the developed deep


layer Raman photons undergo multiple scattering inside the

bulk of the sample.

T4 – T5: At the end of the pulse, Raman photons and fluorescence from

the surface layer starts to fade out. The deeper layer Raman

photons continue to emerge at the surface of the sample but

after a time delay caused by the multiple scattering events that

were experienced within the bulk of the sample [131].

T5 – T6: By shifting the detector gate delay in time, to a region between

T5 and T6, the detector can be synchronised with the arrival of

the delayed deeper layer Raman photons. In doing so, the

spectrometer becomes capable of selectively detecting the

Raman photons from the deeper layers of a sample as a

function of time.

It is imperative to note that there is no standard gate delay that may be applied

to all samples. The optimum gate delay for a sample depends on various factors

which include the distance between the instrument and the sample, the laser power

density that is incident on the surface of the sample, the temporal resolution of the

gated detection system as well as the optical characteristics of the packaging material

and the concealed substance such as the optical density and refractive index [111,

122, 127, 128].

The temporal resolution of the detector is a key factor which deserves much

attention. A detector with a high temporal resolution facilitates the detection of

Raman photons arising from the deeper layer while effectively rejecting the photons

of the surface layer through the use of a narrow gate width. However, the SNR is

inadvertently reduced due to the detection of a low number of photons. A larger gate

width would capture more photons, but may also capture remnants of the Raman

photons and fluorescence from the surface layer. The following study is thus a means

to gauge the effectiveness of implementing a larger gate width of 4ns within a

nanosecond-scale system. The choice of gate width was based on preceding

experiments which indicated that a 4ns gate width provided a maximum Raman


signal whilst minimising contributions from fluorescence as well as background

lighting.

4.4 STAND-OFF TRRS DETECTION STUDY

4.4.1 Preliminary TRRS Analysis

A preliminary analysis was performed on ammonium nitrate from 3m

concealed in two different polymer materials; an opaque white 2mm thick HDPE

container and a fluorescing opaque yellow 2mm thick polystyrene container. The

results obtained were utilised to study the degree of selectivity for the deeper layer as

well as the signal-to-noise ratio (SNR)

Ammonium Nitrate Concealed in White Plastic Container

Figure 4.3: Raman spectra of ammonium nitrate concealed in the white container acquired from 25ns

to 65ns

Ammonium nitrate was concealed in a 1.5mm thick white opaque HDPE

container and TRRS was attempted from a stand-off distance of 3 metres. The

instrumental configuration utilised for TRRS has been detailed in section 3.1.1. A set

of spectra was procured at gate delays ranging from 25ns to 65ns. The resulting

Raman Shift [cm-1]

Inte

nsit

y [C

ount

s]


spectral profile at different gate delays is presented in figure 4.3. As indicated by the

figure, the intensity of the Raman signals gradually increases with the detector gate

delay to reach a maximum at 41ns. To facilitate a closer inspection of the relative

contributions from ammonium nitrate and the HDPE polymer, seven spectra at 3ns

intervals are presented in figure 4.4 where the Raman signal of ammonium nitrate is

highlighted in blue while one of the peaks that is characteristic of the HDPE polymer

is highlighted in grey.

At 32ns, the spectrum features high Raman signal contributions from the

HDPE polymer (container wall). However, as the detection gate is temporally shifted

(i.e. increasing gate delays), a relative variation is observed in the proportion of the

Raman signals from the HDPE polymer and ammonium nitrate; specifically, a

gradual suppression of the spectral features belonging to the HDPE polymer is

notable relative to the Raman signal of ammonium nitrate. The suppression proceeds

to an extent where there is minimal contribution from the container and a markedly

higher contribution from ammonium nitrate as indicated in the spectrum obtained at

a gate delay of 50ns.

Utilising the set of spectra listed in figure 4.4, the degree of selectivity for the

deeper layer was determined based on the ratio of the Raman signal of ammonium

nitrate at 1020cm-1

(highlighted in blue) to the Raman signal of the HDPE polymer at

1100cm-1

(highlighted in grey) and plotted as a function of time (Figure 4.5). The

resulting trend indicates an exponential increase in the signal intensity ratio with

increasing gate delays. The signal intensity ratio at a gate delay of 50ns was

determined to be 8.33. Despite the minimal contributions from the container, TRRS

was capable of the temporal resolution of the Raman photons arising from the

content.


Figure 4.4: TRRS of ammonium nitrate in a white HDPE container


Figure 4.5: Signal intensity ratio as a function of gate delays for the TRRS analysis of ammonium

nitrate concealed in a white HDPE container

A graphical plot of the SNR of the ammonium nitrate and the HDPE polymer

as a function of the gate delay is presented in figure 4.6. SNR of the spectra were

determined based on the ratio of the ammonium nitrate peak intensity to the root

mean square (RMS) value of the noise in each spectrum. The SNR of ammonium

nitrate was observed to reach its peak at a gate delay of 44ns before falling off. As

previously illustrated in figure 4.2, the targeted time frame at which minimal to no

Raman signal from the surface is present has a low Raman photon count. As a result,

the SNR of the spectra retrieved within this region where minimal Raman signals

from the container exist is significantly low.

Figure 4.6: Signal-to-noise ratio as a function of gate delays for the TRRS analysis of ammonium



Ammonium Nitrate Concealed in Yellow Plastic Container

The analysis was repeated on ammonium nitrate while it was concealed in the

yellow polystyrene container. Procured results are presented in figure 4.7. A gradual

suppression of the Raman signal of the polystyrene polymer at 1585cm-1

is observed

relative to the ammonium nitrate Raman signal at 1020cm-1

. However, unlike the

white container that was previously utilised, the Raman signal of the polystyrene

polymer is persistent throughout the spectra to the extent that its contribution is still

apparent at a gate delay of 50ns.

The signal intensity ratio plot in figure 4.8 was obtained based on the ratio of

the ammonium nitrate peak at 1020cm-1

to the polystyrene polymer peak located at

1585cm-1

. The observed trend was similar to that of the ammonium nitrate in the

HDPE white container (Figure 4.4). A significantly lower signal intensity ratio of

1.84 was obtained at 50ns when compared to that obtained from the HDPE container.

This observation may be attributed in part to a broader temporal profile exhibited by

the polystyrene polymer.

Figure 4.9 presents the resulting SNR plot as a function of the gate delay. The

trend is similar to that obtained from the white HDPE container. In order to retrieve a

spectrum of ammonium nitrate alone, a scaled subtraction is performed between two

spectra of varying relative contributions as demonstrated in (Figure 4.10). The

scaling procedure involves the use of a baseline correction by weighted least squares

followed by normalising both spectra to the maximum intensity of the polystyrene

polymer at 1585cm-1

. The relative differences between these two spectra facilitate

the retrieval of a spectrum that is solely characteristic of ammonium nitrate via

scaled subtraction. The stand-off detection of ammonium nitrate in the yellow

polystyrene container by TRRS was repeated at 8 metres. Similar results for those at

3 metres were obtained. The scaled subtracted TRRS of ammonium nitrate at 8

metres is presented in figure 4.11. Identifying the spectral contribution of the surface

layer (polystyrene polymer) can be achieved without prior knowledge of the

composition of the packaging material. This can be done by observing the changes in

the signal profile in terms of the relative intensities of the spectral lines as a function

of the detector gate delay. For instance, the initial spectra indicate strong

contributions from the polystyrene polymer which are gradually suppressed with the

shift of the detector gate in time (i.e. increasing gate delay). Meanwhile the


ammonium nitrate spectral contribution increase to dominate the acquired TRRS

spectrum. This is of significant value for forensic investigations of suspected

packaging where the composition of the packaging material is usually unknown.

Figure 4.7: TRRS analysis of ammonium nitrate in a yellow polystyrene container



nitrate concealed in a yellow polystyrene container




Figure 4.10: Demonstration of a scaled subtraction between two spectra obtained at different gate

delays for the ammoniu nitrate concealed in a yellow polystyrene container

Figure 4.11: TRRS spectrum of ammonium nitrate detected from 8 metres. A scaled subtraction was

performed between spectra obtained at gate delays of 76ns and 79ns

Summary of Preliminary Analysis

The preliminary analysis of ammonium nitrate concealed within two different

containers has demonstrated the ability of TRRS to interrogate deeper layers of a

sample by temporally resolving the Raman photons arising from the concealed

substance. However, the efficiency of TRRS and the resulting quality of the

spectrum are factors that have to be considered, especially when dealing with a

packaging material that exhibits intense diffusely scattering properties. In such a

scenario, the gate delay at which a clean spectrum is achieved would exhibit a

significantly low SNR. Alternatively, a scaled subtraction may be performed utilising

two spectra that exhibit variations in the Raman signal intensities of the respective

layers.


4.4.2 Stand-off TRRS Detection at 3 metres

Stand-off TRRS detection of acetylsalicylic acid, 2,2-thiodiethanol, GBL and

30 % H2O2 were carried out whilst the samples were concealed in a white opaque

2mm thick HDPE container at a distance of 3 meters. The results are shown in

figures 4.12, 4.13, 4.14 and 4.15 respectively, presenting only the spectra obtained

from 41ns onwards for brevity. As indicated by figure 4.12, TRRS was capable of

temporally resolving the Raman signal of aspirin from the spectral contribution of

the container material.

The degree of challenge in dealing with liquid samples is higher due to the

manner in which Raman scattering takes place within liquids as well as the photon-

diffusion mechanics in a transparent medium that is concealed within a diffusely-

scattering medium [132]. The resulting spectra for both liquid samples indicate that

TRRS was efficient in suppressing the Raman signals from the container to a

sufficient degree for the identification of the respective (Figure 4.13 – 4.14). Finally,

TRRS was utilised to detect 30% v/v aqueous H2O2 in a white HDPE container

(Figure 4.15). Despite the high water content of the sample and the weak Raman

scattering properties of H2O2, TRRS was capable of providing a spectrum at 50ns

which has minimal contributions from the container. These results highlight the

potential of the technique for the detection of drugs, illicit substances, chemical

warfare and peroxide-based explosives in suspected packaging without the need to

disturb the packaging by invasively probing the exhibit.

‎Chapter 4: Time-Resolved Raman Spectroscopy 58

Figure 4.12: TRRS analysis of aspirin concealed in a white HDPE container Figure 4.13: TRRS analysis of 2,2-thiodiethanol concealed in a white

HDPE container


Figure 4.14: TRRS analysis of GBL concealed in a white HDPE container Figure 4.15: TRRS analysis of hydrogen peroxide concealed in a white

HDPE container


4.4.3 Stand-off TRRS Detection at 15 metres

With the successful results demonstrated in the previous sections, the working

distance was increased to 15m. Figures 4.16 – 4.18 present the TRRS results of 2,4-

DNT, nitromethane and ammonium nitrate respectively. The explosive precursors

were concealed in a white opaque 1.5mm thick HDPE container and measured from

a distance of 15m. It can be noticed from the acquired spectra that there was a

gradual suppression of the Raman signals arising from the container.

Figure 4.16: TRRS analysis of DNT concealed in a white HDPE container


Figure 4.17: TRRS analysis of nitromethane concealed in a white HDPE container Figure 4.18: TRRS analysis of ammonium nitrate concealed in a white HDPE

container


4.5 CONCLUSION

The depth profiling capability of TRRS has been demonstrated throughout this

chapter at working distances from 3m to 15m. Preliminary results have indicated the

limited efficiency in the retrieval of the deeper layer when the temporal profile of the

packaging material is broadened such that it coincides with the tail end of the

temporal profile of the deeper layer. Even though the signal intensity ratio increased

exponentially with the detector gate delay the SNR in the acquired spectrum reduces

significantly. A scaled subtraction was suggested as the resolution to such a scenario.

Subsequent analyses of liquid samples have confirmed the applicability of TRRS to

detect Raman photons from the deeper layer. Stand-off detection analysis at a

working distance of 15m was also demonstrated. Towards the end of this research

endeavour, a study by Zachuber et al was published demonstrating the utility of

stand-off TRRS for the detection of concealed samples from a working distance of

40m [133]. The results indicated in the study are congruent with the results presented

throughout this chapter.

‎Chapter 5: Spatially-Offset Raman Spectroscopy 63

Chapter 5: Spatially-Offset Raman Spectroscopy

5.1 INTRODUCTION

Spatially-Offset Raman Spectroscopy (SORS) facilitates the depth profiling by

spatially discriminating between Raman photons from the surface layer and those

from the deeper layer of a sample. This is carried out be implementing a spatial

offset between the laser-excited zone and the Raman collection zone on the surface

of a sample. The spatial offset facilitates the retrieval of a higher proportion of the

deeper layer Raman photons whilst suppressing the surface layer photons [110,

134].

5.1.1 Conventional Continuous Wave (CW) SORS

The initial SORS concept was pioneered by Matousek et al [110, 134]. In CW

SORS, a spot-sized area on the surface of the sample is excited with a continuous

wave laser beam and the generated Raman photons are collected from an area that is

laterally offset, by a distance (ΔS), from the laser-excitation point [110]. The

collected radiation is detected by means of a non-gated CCD detector. When a laser

beam interacts with a sample that consists of a diffusely scattering surface layer and

a deeper layer (e.g. a chemical substance), the excitation photons propagate into the

deeper layer in a random walk-like fashion. The Raman photons that generate from

the deeper layer would experience a significantly large number of scattering events

within the bulk of the sample. These multiple collisions completely randomise the

direction of the deep layer Raman photons (Figure 5.1a). Due to the random

scattering of the photons within the deep layers of the sample, the excited area within

the bulk of a sample increases with the sample depth [110]. Additionally, the deep

layer Raman photons traverse back to the detector with wider distribution profiles

than that of the surface layer photons (Figure 5.1b). Due to this wider distribution,

the resulting intensity profile of the deep layer Raman photons is lower than that of

the surface layer photons (Figure 5.1c). As indicated in figure 5.1c, when the

collection system coincides with the illumination source (zero offset), a significantly

high proportion of the surface layer Raman and fluorescence photons is detected. In

Chapter 5: Spatially-Offset Raman Spectroscopy 64

this case the surface layer contributions overwhelm the acquired spectrum. However,

when‎ the‎photon‎ collection‎point‎ is‎ positioned‎at‎ an‎ arbitrary‎offset‎ (ΔS)‎ from the

point of illumination (for instance, at x1), the proportion of Raman photons from the

surface becomes comparatively lower than it would be at a zero offset. The lateral

offset also effectively discriminates against photons propagating sideways within the

surface layers as they exhibit a higher loss at the air-to-sample interface than photons

propagating through deeper layers [81]. Consequently, the SORS technique

suppresses the interfering Raman and fluorescence signals originating from the

surface layer [134]. As indicated in figure 5.1c, by implementing a larger offset (x2),

a higher proportion of the deeper layer photons can be detected. However, due to the

significantly low number of Raman photons at this position, the resulting spectrum

exhibits a low SNR [135]. Alternatively, a spectrum that represents the deep layer

only can be acquired by procuring at least two Raman spectra; at a zero‎ and‎ ΔS‎

offsets. A scaled subtraction of the acquired spectra can be carried out using

appropriate algorithms to eliminate the residual spectral contributions of the surface

layer [81, 110, 136].

Since the demonstration of SORS, extensive developments have been

undertaken to enhance the quality of the spectral results acquired. Successful

applications of the use of an array of fibers within the collection scheme [137] led to

the adaptation of a concentric fiber arrangement to the collection scheme in SORS

[138]. This scheme has not only increased the collection efficiency of SORS but has

also facilitated the use of lasers at a lower power density which is particularly useful

for the in-vivo analyses of deep tissue layers where the permissible exposure to skin

tissue is of great concern [138].


Figure 5.1: Illustration of the spatial effects of Raman photons undergoing diffused scattering in a two

layered diffusely scattering medium


5.1.2 Inverse SORS

In contrast to the conventional SORS scheme that utilises two sets of fiber

arrangements to retrieve two spectra from different offsets, inverse SORS

manipulates the shape illumination beam from a spot (for zero offset measurements)

to that of a ring (for offset measurements) [139]. As such, only one set of fibers are

positioned at the middle of the probe, allowing both measurements to be binned onto

the same CCD track. In doing so, the spectral artefacts that are commonly

encountered in conventional SORS upon scaled subtractions are avoided when

utilising inverse SORS [139]. Inverse SORS also facilitates the direct coupling of the

Raman collection system to the spectrograph which exhibits enhanced collection

efficiency of the Raman photons as opposed to fiber coupling [136].

5.1.3 Transmission Raman Spectroscopy

Transmission Raman spectroscopy is regarded as an extreme variant of SORS

where the offset between the collection and illumination zones is maximized by

setting the Raman photon collection optics at 180 ° from the laser excitation beam

[112]. Transmission Raman spectroscopy exhibits significant tolerance towards the

thickness of the sample, enhanced suppression of fluorescence as compared to SORS

as well as the provision of a representative spectrum of a stratified sample allows for

efficient qualitative and quantitative analyses of concealed samples [112, 140-146].

Transmission Raman is be best suited for quality control applications where the

spectral profile of the samples and their packaging materials are known.

5.1.4 Applications of CW SORS

SORS has been demonstrated in biomedical studies for the probing of deep

layers of human tissue [138, 147-150], animal tissue [151, 152] as well as the

detection of calcifications in breast tissues which have demonstrated the use of

SORS as being a potential tool for cancer research. Potential applications of SORS in

forensic and homeland security investigations have also been discussed and

demonstrated. Explosives in the form of solids (ammonium nitrate, 2,4-

dinitrotoluene, sodium perchlorate) and liquids (hydrogen peroxide and


nitromethane) concealed in various types of coloured and non-coloured packaging

have been demonstrated with positive results [132, 136, 153, 154]. Additionally,

Eliasson et al demonstrated the non-invasive detection of liquid explosives

(hydrogen peroxide) dissolved in commercial personal care products. SORS has also

been demonstrated for the identification of authentic and counterfeit pharmaceutical

products as well as illicit drugs in the form of tablets, capsules and suspensions [2,

81, 155-157]. The utility of SORS facilitated the non-invasive analysis of these

products without the need to tamper with the packaging material thus maintaining the

credibility of the product.

As indicated, numerous studies have been performed towards the qualitative

identification of a concealed substance where the technique has been utilized to the

detection of a single component in a non-Raman active or weak Raman scattering

matrices. Previous work by researchers in this art was mainly focused on

demonstrating the ability of SORS to suppress the Raman contributions from a

surface layer and, in effect, obtain the native spectrum of the concealed hazardous

single component. However, no attempt has been made to apply robust chemometric

techniques to SORS measurements to concealed complex mixtures that are

constituted of more than one Raman active component. When it comes to the

detection of a deep layer that consists of more than one Raman active component, the

SORS technique alone cannot distinguish between the various components of the

deep layer,. In fact the SORS technique in this scenario only yields a combined

spectrum that represents the different components of the deep layer. There is a vital

need to combine the SORS technique with multivariate statistical analysis in order to

identify and quantify the different components of the deep layer of a sample. By

achieving this goal, a new dimension can be added to the SORS technique where the

technique will be utilized to the detection and quantification of concealed hazardous

mixtures and not only a single compound. That is to say, when SORS is combined

with chemometrics, the acquired SORS spectrum of a concealed deep layer of a

sample can be further analysed to identify and predict the concentration of the

various components of the deep layer. This new dimension of SORS application

would make the technique highly valuable from a practical point of view . This is

because most of the real life samples are in fact complex mixtures where the

concealed hazardous substance is frequently composed of a mixture of diluents,


masking agents, adulterants and the active component (e.g. explosive precursor or

illicit material). This study attempts for the first time to extend the application of

SORS to the detection and semi- quantitative prediction of the various components

of a concealed mixture (representing the components of a deep layer of a sample).

5.1.5 Pulsed Wave (PW) SORS

Zachhuber et al recently extended SORS to stand-off measurements of

concealed chemical substances [158]. They utilized a pulsed laser excitation and a

fast gated detection system for the detection of concealed sodium chlorate as well as

isopropanol from a distance of 12m. Stand-off SORS was carried out for samples in

non-coloured HDPE packaging and required precise translation of the excitation

beam onto the sample surface to different offset points from the collection system.

5.2 AIMS

- To utilise CW SORS for the detection of concealed substances in non-coloured

and coloured packaging materials from a non-contact distance of 6cm.

- To conduct a feasibility study of utilising chemometric techniques on spectral

data acquired from SORS for the purpose of qualitative categorisation and semi-

quantification of pharmaceutical drugs, formulations and narcotics.

- To demonstrate the applicability of pulsed SORS for the stand-off detection of

concealed substances at working distances of up to 15m.

- To determine the degree of selectivity for the deeper layer as well as the signal-

to-noise ratio within the acquired spectra.


5.3 CONTINUOUS WAVE (CW) SORS ANALYSIS

The demonstrations and analysis throughout this section utilises an inverse-

SORS mode due to the advantages exhibited by this configuration over conventional

SORS which have been highlighted in section 5.1.2.

5.3.1 Demonstration of CW SORS Data Treatment

The instrumental configuration for CW SORS has been detailed in section

3.1.2. A typical CW SORS procedure entails the retrieval of two measurements at

two different offsets. The first measurement is performed with the laser focused as a

spot on the container (zero offset). The second measurement is performed with the

laser projected as a ring. In this setting the spatial offset between the laser excitation

beam‎ and‎ the‎ collection‎ zone‎ (ΔS)‎ will‎ be‎ the‎ radius‎ of‎ the‎ ring.‎ An‎ example‎ is‎

presented in figure 5.2 of the resulting spot and ring measurements on

acetaminophen concealed in a 2mm thick white polypropylene container.

Figure 5.2: Demonstration of spot and ring measurements using CW SORS


The spectral profiles of the spot and ring spectra indicate differing levels of

surface layer and deeper layer spectral contributions. The reason behind the ability of

the spot spectrum to capture Raman signals from the deeper layer can be attributed to

the dimension of the excitation laser beam where we used a laser excitation beam of

2 mm diameter as opposed to the significantly smaller diameters utilised by other

researchers [110, 139]. Utilising a wide excitation laser beam reduces the power

density onto the surface of the sample, thus reducing the risk of laser-induced sample

degradation.

The lateral offset created by the ring illumination facilitates the spatial

discrimination of the Raman photons from the deeper layers, thus a higher relative

contribution of the deeper layer is observed in the ring spectrum as opposed to the

spot spectrum. The difference of relative spectral contributions of the surface and

deep layers of the sample allows a scaled subtraction to be performed in order to

retrieve a spectral profile that is representative of the concealed substance only

(Figure 5.3).

Figure 5.3: Demonstration of a scaled subtraction to retrieve a clean spectrum of the

concealed layer


The scaling procedure consisted of the following steps:

1. Baseline correction which is achieved by subtracting each spectrum with

its respective minimum intensity,

2. Normalization of the spectra with respect to their respective intensity at

807cm-1

as it corresponds to the maximum intensity peak of the spot

spectrum. Normalising the spot and ring spectra with respect to the

maximum peak of the surface layer allows for the efficient subtraction of

the spectral features belonging to the surface layer.

3. Scaled subtraction between the normalized spectra.

The scale-subtracted SORS spectrum is characteristic of acetaminophen,

indicating the efficient extraction of the spectral profile characteristic of the

concealed substance with no residual signals belonging to the surface layer.


5.3.2 CW SORS Detection of Concealed Substances under Background

Lighting

A distinct limitation that has not been elaborated in previous SORS research is

the effect of background lighting on the capability of this technique to retrieve

spectral profile of the deep layers of a sample under field conditions where

background light can cause a significant interference to the acquired signal. SORS is

traditionally conducted under dark which is especially impractical for in-field

situations. In order to further optimise the technique for field applications, SORS is

attempted under background lighting conditions. A spatial offset of 7 mm was

utilised while concealed samples were positioned at a non-contact distance of 6 cm

from the Raman collection optics. In order to maximise the number of collected deep

layer return Raman photons that reaches the detector, direct coupling was utilised.

Acetaminophen, hydrogen peroxide, and nitromethane were screened in different

coloured containers and behind coloured garment using NIR excitation and a non-

gated CCD camera.

The concealed samples were measured under incandescent and fluorescent

lighting. Figure 5.4 presents the SORS results under the different background

lightning. As indicated by the figure, the background noise from different light

sources did not prevent the identification of the interrogated chemical substances

within relatively short time periods. This can be attributed to the efficient direct

coupling of the collection optics that allows for the backscattered photons to be

collected into the slit of the spectrograph with minimum losses. However, the signal-

to-noise ratio (SNR) in CW SORS measurements is relatively poor in many cases.

This is due to the inability of the non-gated CCD detector to reject background light.

Thus, the noise and light fluctuations occurring in the background light (particularly

from the incandescent light and sunlight) were impressed upon the SORS spectra,

resulting in a relatively high level of noise in these spectra. Coloured packaging

materials tend to complicate the spectra due to the high occurrence of fluorescence.

Despite the use of a near-infrared (NIR) laser source which reduces the degree of

fluorescence in comparison to the use of a visible range laser source, the presence of

fluorescence‎ is‎ still‎ discernable‎ in‎ both‎ the‎ ‘spot’‎ and‎ ‘ring’‎ spectra‎ albeit‎

significantly reduced. Scaled subtractions additionally aid in cancelling out the effect


of fluorescence, resulting in the SORS spectra with sufficient signal-to-noise ratio for

identification.

Figure 5.4: CW SORS spectra of a) Ammonium nitrate in an off-white plastic bottle

(measured under fluorescent light, SNR=10); b) H2O2 in an off-white shampoo plastic

bottle (measured under incandescent background light, SNR=2); c); H2O2 in a red plastic

bottle (measured under incandescent background light, SNR=4); d) H2O2 in a red plastic

bottle (measured under daylight, SNR=5); e) acetaminophen behind a blue fabric garment

(measured under fluorescent background light, SNR=10)


5.3.3 Qualitative and Semi-Quantitative Analysis of CW SORS Spectral Data

using Chemometrics

Chemometrics and Raman Spectroscopy

Chemometrics is a field of Science which delves into the application of

mathematical and statistical treatments to a set of data procured from a chemical

analysis in order to provide relevant information that aids in the qualitative and

quantitative analysis of the samples being tested [159]. The extensive use of

Chemometrics in Raman spectroscopy has been documented in a significant number

of publications [85]. This section deals with the application of principal component

analysis (PCA) and partial least squares (PLS) techniques to a series of spectra to a

series of SORS spectra in order to elucidate qualitative and quantitative information.

As an unsupervised learning technique, PCA reduces the dimensionality of

multivariate data, such as Raman spectra, and maximises the variance along each

component [159, 160]. In doing so, it facilitates the qualitative discrimination of

samples analysed. PLS aids in the development of prediction models within such

multivariate data which facilitate the quantitative analysis of samples[97, 160].

The use of such techniques can be traced to earlier studies conducted by Ryder

et al where a quantitative analysis was attempted on a group of 20 mixtures,

containing varying amounts of cocaine and glucose, by implementing a partial least

squares (PLS) analysis which resulted in a root mean square error of prediction

(RMSEP) [159] of 2.3% [97]. Following the successful application in the initial

research, a subsequent study was conducted by utilising a 3-part mixture containing

cocaine, glucose and caffeine which resulted in a RMSEP of 4% [96]. The larger

error encountered was attributed to the possible inhomogeneity of the samples being

analysed. Ryder further expanded the applicability of Chemometrics by

implementing a principal component analysis (PCA) on eighty-five powdered

mixtures; each containing one of three illicit substances (cocaine, heroin or MDMA)

along with various diluents to retrieve qualitative data and to determine the

feasibility of implementing such a technique for the rapid classification of illicit

drugs commonly seized by the authorities [161]. This study also described the effects

of implementing different forms of spectral preprocessing on the resulting PCA

plots. However, the efficiency of the classification was indicated to be proportional


to the Raman cross section of the illicit substance. As a result, it was suggested that

an efficient classification would entail the selection of peaks following a set of

spectral preprocessing procedures. In an effort to improve the resulting PCA analysis

of the 85 mixtures as well to conduct a PLS analysis on the same group of mixtures,

Leger and Ryder utilised a proposed preprocessing method introduced by Lieber and

Mahadevan-Jansen [162]. However, only slight improvements were observed in the

qualitative analysis while the quantitative analysis on the mixtures resulted in a high

RMSEP of 8% for heroin and cocaine. It has to be noted that such techniques may be

applied to other mixtures containing pharmaceutical active ingredients and explosive

precursors. Subsequent studies by other research groups led to the improved

efficiency of these techniques [163-172].

One group in particular utilised a sample rotator and a wide area illumination

in an effort to improve the homogeneity of the resulting spectra of mixtures

containing one of four drug surrogates and up to three diluents. Along with the

suggested spectral preprocessing techniques, the resulting PCA analysis indicated the

efficiency in classifying the mixtures based on their respective drug surrogate while

acquiring RMSEP values within the 4% range. The use of a sample rotator aids in

enhancing the homogeneity of the resulting spectra but it also requires the sample to

be physically positioned on the sample rotator.

The research applications that have been mentioned so far are associated with

the Raman measurements made directly on samples that are not concealed. To date,

there has been only one study which utilises a depth profiling technique for the non-

invasive quantitative analysis of a concealed sample. Eliasson et al utilised PLS

analysis on spectra acquired from transmission Raman spectroscopy for the non-

invasive detection of specific active ingredients in fifteen powdered blends

consisting of four components concealed within capsules [173]. The results of the

PLS analysis provided RMSEP values of 1.2% and 1.8% which indicated the

potential of utilising Transmission Raman spectroscopy for the quantitative analysis

of capsules’‎contents.

In this experiment, two sets of mixtures are utilised to simulate illicit and

pharmaceutical formulations. Each set contains a different active ingredient. A series

of measurements by SORS as well as conventional direct Raman spectroscopy was

performed. The spectra obtained by conventional direct Raman spectroscopy are


utilised as a reference to determine the efficiency of SORS as well as to observe any

possible deviation from the true results due to the scaled subtractions that were

performed prior to the analysis.

Experimental Parameters

Two sets of 13 mixtures were prepared (Table 5.1), with concentrations

ranging from 5% to 100% by weight of an active ingredient (acetaminophen or

phenylephrine) with equal amounts of caffeine and glucose which simulated

common excipients/cutting agents in a pharmaceutical/illicit-drug formulation

(Figure 5.5). Acetaminophen and Phenylephrine were simulated to be the active

ingredient for Set A and Set B respectively. Acetaminophen has a larger Raman

cross section than phenylephrine which aids in investigating the tolerance of this

technique to more challenging samples. Acetaminophen, phenylephrine and caffeine

were procured from Sigma-Aldrich while anhydrous D-glucose was procured from

Chem-Supply. The mixtures were prepared by, first, weighing the appropriate

amounts of the specified components, then mixing and homogenising the

components into two sets of mixtures (Set A and Set B).

Figure 5.5: Reference spectra of the respective components utilised for Set A and Set B

700 800 900 1000 1100 1200 13000

0.5

1Acetaminophen Reference

700 800 900 1000 1100 1200 13000

0.5

1

Phenylephrine Reference

Inte

nsi

ty [

Counts

]

700 800 900 1000 1100 1200 13000

0.5

1Caffeine Reference

700 800 900 1000 1100 1200 13000

0.5

1Glucose Reference

Raman Shift (cm-1)


Figure 5.6: Setup of SORS and alignment of the sample concealed in a container

Set A: Acetaminophen Set B: Phenylephrine hydrochloride

Sample

Acetaminophen

(%w/w)

Caffeine

(%w/w)

Glucose

(%w/w)

Sample

Phenylephrine

hydrochloride

(%w/w)

Caffeine

(%w/w)

Glucose

(%w/w)

A1 5.16 47.50 47.34

P1 5.10 47.40 47.49

A2 10.24 44.96 44.80

P2 10.02 45.06 44.92

A3 15.19 42.42 42.38

P3 15.02 42.32 42.66

A4 34.92 32.67 32.41

P4 34.93 32.45 32.62

A5 40.02 30.18 29.81

P5 39.98 30.00 30.02

A6 45.01 27.70 27.28

P6 44.85 27.61 27.54

A7 49.88 25.25 24.87

P7 49.64 25.27 25.09

A8 64.78 17.69 17.53

P8 64.76 17.80 17.45

A9 69.73 15.30 14.97

P9 69.72 15.21 15.07

A10 74.59 13.04 12.36

P10 74.62 12.66 12.71

A11 89.60 5.45 4.95

P11 89.47 5.23 5.31

A12 94.61 2.90 2.49

P12 94.42 2.72 2.85

A13 100.00 - - P13 100.00 - -

Table 5.1: Compositions of the two sets of mixtures with Set A and Set B containing acetaminophen

and phenylephrine hydrochloride respectively as the active ingredient.


For the non-invasive SORS measurements, aliquots of the prepared mixtures

were concealed within a white, opaque polypropylene container of ~2mm thickness

(figure 5.6). For comparison, other aliquots of the tested mixtures were

accommodated in an open top metallic vessel and measurements of the unconcealed

samples were performed by conventional direct Raman spectroscopy each mixture.

Three replicate measurements were performed at the same position for each

mixture. PCA and PLS analyses were applied to the acquired the SORS and direct

Raman spectra. Each spectral measurement was performed using 30 accumulations

of 2s exposures, and a total measurement of 1 minute per sample. All spectra were

background corrected and acquired within the spectral range of 634 to 1330cm-1

,

accumulating a total of 1024 data points. The measurements were carried out under

dark conditions and the resulting spectra were background corrected.

Matlab R2009b (The Mathworks) was utilised to carry out scaled subtractions

of the relevant SORS spectra in order to obtain scaled-subtracted SORS spectra that

represents the content only. Multivariate analyses such as PCA and PLS, along with

preprocessing of the spectral data were performed using PLS_toolbox 6.2.1

(Eigenvector Research Inc.).

Preprocessing Methods

Figure 5.7: Preprocessing techniques performed on spectra obtained from Set A

The spectra that were obtained from set A and set B mixtures, were first

subjected to pre-processing prior to PCA and PLS. The main purpose of pre-


processing the spectral data was to minimise the variations within the acquired

spectra that do not reflect the changes in the concentration of the active ingredients

of the mixtures such as instrumental fluctuations. This results in the enhancement of

the variations that directly correspond to the changes in concentration. Figure 5.7

demonstrates the pre-processing techniques that were implemented on the spectral

data of Set A. The raw data consisted of all spectra obtained from every sample

inclusive of its replicates. The accumulated spectra showed a distinct baseline

variation between the spectra which is pronounced between 634cm-1

to 840cm-1

. A

baseline correction, in the form of weighted least squares using a second order

polynomial was performed to rectify the floating baseline. Additionally, mean-

centering was implemented on the baseline corrected spectra. Mean centering was

achieved by subtracting the mean value within each column of the data matrix

(where each column represents the intensity values at a specific wavenumber) from

every value within the same column. This is repeated for all columns of data. The

resulting data is a set of relative numerical values that indicate the extent of deviation

of each value from the mean value, thus scaling the entire set of data. The resulting

pre-processed data was utilised for the subsequent PCA and PLS analyses.

Qualitative Analysis using PCA

In order to determine the appropriate number of dimensions, or principal

components, to describe the inherent variation of the data Root Mean Square Error of

Cross Validation (RMSECV) was performed using the preprocessed sets of data. The

resulting eigenvector plot (Figure 5.8) indicated that three principal components were

sufficient to describe the variation of the entire data set. Subsequent principal

components would take into regard the noise derived from the spectra. This is

indicated by the point at which the plot levels off.


Figure 5.8: Eigenvector plot for PCA analysis

The principal components 1, 2 and 3 accounted for 82.95%, 13.93% and 2.34%

of the variation respectively, amounting to 99.23% of cumulative variance of the

entire data set (Figure 5.9). PC1, which accounts for the largest variance, was

dominated by acetaminophen peaks due to its relatively higher Raman scattering

properties among all the compounds used in this analysis. PC2 appeared to be a

combination of phenylephrine and acetaminophen. PC3 accounts for the lowest

variance and was essentially an amalgamation of all four compounds in the order of

phenylephrine, acetaminophen, caffeine and glucose based on their respective

intensity profile.


Figure 5.9: PCA scores plot utilising a) PC1 and PC2, b) PC1 and PC3

Samples from set A are distinguished by the red triangle markers while samples from set B are

distinguished by the green crosses.


The scores plot for PC1 vs PC2 indicated a distinct separation between the two

sets of mixtures. For each set of mixtures, there was an observable linear pattern,

which reflected the increasing concentration of the respective active ingredient.

Equal amounts of excipients (caffeine and glucose) were present in every

concentration of the mixtures prepared. At lower sample concentration levels (5% -

15%) a larger amount of the excipients were present at equal amounts. It is for this

reason that the prominent spectral peaks within this range are those belonging to the

excipients. Consequently, the spectral profiles within this low concentration range

for both acetaminophen (Set A) and phenylephrine (Set B) were similar thus

positioned within close proximity in the scores plot.

Despite the larger proportion of the excipients in the lower concentration

ranges of the samples, SORS was still able to probe through the polypropylene

container and detect the peaks derived from acetaminophen and phenylephrine

respectively. This is proven by the distinct separation between the low concentration

samples of both sets in the scores plot. If SORS was not able to detect the active

ingredients at such low concentrations, then only the spectral profiles of the diluents

would remain and the scores plot would indicate some form of proximal overlapping

of the low concentration samples from both sets. With increasing concentrations of

the respective active ingredient, the difference in the spectral profiles between Set A

and Set B enhances. This can be observed by the divergence of the data points in the

scores plot with increasing concentration of the active ingredient, resulting in a v-

shaped pattern.

The scores plot of PC1 vs PC3 shows a relatively more distinct separation

between the two sets of mixtures. The linear pattern for each set was still present,

though not as clearly defined as that in the scores plot of PC1 vs PC2. However, a

notable difference was the order of concentration for either set where the order of

concentration proceeds in opposing directions of either set. This is attributed to PC 3

where the loadings describe an amalgamated spectrum of all the components with

relatively higher contributions of phenylephrine, caffeine and acetaminophen.


Figure 5.10: Loadings plots for PC1, PC2 and PC3

The loadings plots for the respective principal components are presented in

figure 5.10. The loadings for PC1 bore a characteristic resemblance to

acetaminophen. Smaller contributions of caffeine, at 740cm-1

, 1025cm-1

and 1080cm-

1, as well as phenylephrine, at 1000cm

-1, were also observed. This indicated that the

discrimination along PC1 was strongly based on the spectral profile of

acetaminophen. The loadings for PC2 bore the spectral features of both

acetaminophen and phenylephrine as indicated by the increase in the peak at

1000cm-1

which is characteristic of phenylephrine. Finally, the loadings for PC3

indicate spectral features from all components of the mixtures. However, it only

explains a small amount of variance (2.34%).

The PCA results indicated that spectral data acquired from SORS can be

utilised for discriminating between mixtures based on the active ingredients that was

present. Additionally, supervised learning techniques may be adapted and applied for

rapid identification purpose.


Quantitative Analysis by Partial Least Squares (PLS) Regression

PLS analysis was conducted on set A and set B separately. Prior to conducting

PLS regression on the spectral data acquired for sets A and B, the entire data set was

subjected to the same preprocessing and cross validation procedures similar to those

conducted for the PCA analysis.

Figure 5.11: Cross validation results for (a) set A and (b) set B

The cross validation results (Figure 5.11) for both acetaminophen and

phenylephrine indicated that two latent variables were sufficient to describe the

variance in the data with minimal noise. Of the thirteen mixtures that were prepared

for each set, eight of the mixtures were utilised as the calibration set while the other

five mixtures were utilised as the prediction set (Table 5.2). All three replicate

measurements were utilised for each sample concentration which amounted to a total

of 24 spectra that were used for the calibration set and 15 spectra that were used for

the prediction set.


Calibration set Prediction Set

Concentration

(%w/w)

5% 10%

15% 40%

35% 50%

45% 70%

65% 95%

75%

90%

100%

Table 5.2: Specifications of mixtures from Set A and Set B allocated to calibration and prediction sets

The PLS regression models for acetaminophen and phenylephrine were utilised

to determine the concentration of their respective validation sets (Figure 5.12). The

calibration plot in itself provided a good linear relationship for acetaminophen (R2 =

0.992) and phenylephrine (R2 = 0.995). The acetaminophen model indicated a root

mean square error of prediction (RMSEP) of 3.8% while that of phenylephrine

provided a RMSEP value of 4.6%.

Figure 5.12: PLS regression model for the quantitative determination of (a) acetaminophen and (b)

phenylephrine


Two latent variables (LV) were utilised for both set A and Set B as they

accounted for a total variance of 99.43% and 97.62%. For set A, LV1 accounted for

98.80% while LV2 accounted for 0.63% of the total variance (Figure 5.13a). For set

B, LV1 accounted for 94.95% while LV2 accounted for 2.67% of the total variance

(Figure 5.13b). The loadings for both set A and set B indicate that the active

ingredients (acetaminophen for set A and phenylephrine for set B) exhibit dominant

spectral features for both LVs. This indicates that the discrimination between the

samples was largely based on the amount of active ingredients present in each set of

mixtures.

Figure 5.13: Loadings of LV1 and LV2 for a) Set A and b) Set B

Bearing in mind that this quantitative analysis is performed based on the

spectra obtained non-invasively through a polypropylene container; the surface layer

(walls of the container) attenuates the incoming and outgoing photons. Therefore, in

order to investigate the degree of deviation in the quantitative results presented thus

far, the quantitative analysis was repeated on the same sets of mixtures but whilst the

mixtures were not concealed. The samples were placed into a metallic dish and direct

Raman measurements at zero spatial offset were carried out. Three replicate spectra

were obtained from each sample mixture. The above-mentioned preprocessing as

well as data treatment procedures were applied to the acquired spectra. The RMSEP

values obtained for the unconcealed mixtures were 3.2% and 5.2% for


acetaminophen and phenylephrine respectively. A small deviation of 0.6% was

observed for the concealed mixtures relative to the unconcealed mixtures. This small

deviation indicated that SORS was tolerant to the signal attenuation caused by the

polypropylene surface layer (container wall) which confirms the applicability of

SORS to the quantitative analysis of concealed substances.


5.4 STAND-OFF SORS DETECTION STUDY

A recent publication by Zachhuber et al demonstrated the applicability of

SORS for stand-off detection, for the first time, by utilising a pulsed laser excitation

source and a gated detection system for the identification of concealed sodium

chlorate as well as isopropanol from a distance of 12m [158]. In this study, a similar

geometry is adopted by utilising the existing instrumentation that has been detailed in

section 3.1.1.

5.4.1 Preliminary SORS Analysis

A preliminary analysis was performed on ammonium nitrate from 3 metres

concealed in two different polymer materials; an opaque white 2mm thick HDPE

container and a fluorescing opaque yellow 2mm thick polystyrene container. The

results obtained were utilised to study the degree of selectivity for the deeper layer as

well as the signal-to-noise ratio (SNR). In order to create lateral offsets between the

point of illumination and collection, the laser beam was shifted horizontally relative

to the point of collection. Based on the work conducted by Zachuber et al, the ICCD

and the laser was synchronised to receive the maximum Raman signal [158]. This

temporal position coincided with the delay at 41ns, as indicated in section 4.5. As

such, spectra were acquired at 41ns with no gate delays implemented to the system.

Ammonium Nitrate Concealed in a White Container

To investigate the relationship between the selectivity towards the deep layers

of a sample and the change in the spatial offset in pulsed SORS, several spectral

measurements were conducted at varying offsets. The spatial offset between the laser

excitation beam and the Raman collection zone was changed from zero to 25 mm

with 5 mm increments and SORS spectrum was collected at each offset. The

resulting spectra (fig 5.14) indicate the effect of increasing the spatial offset on the

relative contributions of the surface and deeper layers.


Figure 5.14: SORS analysis of ammonium nitrate in a white HDPE container


The spectrum obtained at zero offset showed spectral contributions from

ammonium nitrate at 1020cm-1

and the HDPE polymer at 550, 1100, 1260, 1450 cm-1

respectively. However, as the spatial offset was increased, the intensity of the HDPE

polymer signals decreased sharply at a significant rate while that of ammonium

nitrate decreased only slightly at a much slower rate. Therefore the spectral

contributions of the surface layer into the SORS spectrum were significantly

suppressed and those of the deep layer were indirectly enriched. The Raman signal of

ammonium nitrate is clearly distinguishable at the 25mm offset with minimal

contributions from the HDPE polymer. In order to obtain a SORS spectrum that is a

representative of the ammouim nitrate content alone a scaled subtraction of the

spectrum obtained at 15 mm offset from the spectrum obtained at a zero offset was

carried out and the result is shown in figure 5.15.

Figure 5.15: Demonstration of a scaled subtraction between two spectra obtained at different offsets

for ammonium nitrate concealed in a white HDPE container

The signal intensity ratio of ammonium nitrate ate 1020 cm-1

to the HDPE

polymer at 1100 cm-1

was calculated at different offsets and plotted against the

spatial offset. The signal intensity ratio as a function of the spatial offset is presented

in figure 5.16. The signal intensity ratio reached a maximum of 11.11 at a spatial

offset of 25 mm. The maximum signal intensity ratio that was achieved by SORS

was 1.3 times than that achieved by TRRS where the maximum signal intensity ratio

value was 8.33 at 50 ns detector gate delay.


Figure 5.16: Signal intensity ratio as a function of spatial offsets for the SORS analysis of ammonium


The SNR plot featured an increasing trend with increasing spatial offsets

(Figure 5.17). In comparison to TRRS a significantly higher SNR was observed for

the SORS spectrum attained at 25 mm offset. Maher and Berger demonstrated that

the SNR in SORS does not increase monotonically with increasing spatial offsets,

but increases to a maximum before decreasing again [135]. This trend may have been

observed if additional offsets were implemented in the experiment.

Figure 5.17: Signal-to-noise ratio as a function of spatial offsets for the SORS analysis of ammonium nitrate

concealed in a white HDPE container


Ammonium Nitrate Concealed in Yellow Container

To further investigate the capability of pulsed SORS to discriminate against

Raman and fluorescence radiation from the surface layer of a sample, standoff

detection was repeated on ammonium nitrate concealed the fluorescing yellow

polystyrene container at 3 meters. The measurements were carried out at various

spatial offsets in order to investigate the effect of the spatial offset on the selectivity

of pulsed SORS towards the deep layers of a sample. Measurements were made from

a zero offset to a maximum of 50 mm spatial offset with increments of 10mm. The

resulting spectra are presented in figure 5.18

Similar to the previous results, a gradual suppression of the polystyrene

polymer Raman signal at 1585cm-1

was noticeable. At 50 mm offset, the spectral

contributions of the polystyrene polymer into the acquired SORS spectrum were

significantly suppressed. The surface layer photons suppression by SORS at 50 mm

was more significant than that by TRRS at 50 ns detector gate delay where, the

spectral contributions of the polystyrene polymer were still apparent in the acquired

TRRS spectrum. This indicates that when a proper spatial offset is utilised, the

Raman signals from the packaging material can be suppressed more efficiently by

SORS as opposed to TRRS (fig 4.7).

The signal intensity ratio in the standoff SORS measurements of ammonium

nitrate in the polystyrene container exhibited an exponential increase with increasing

the spatial offset (fig. 5.19). A maximum signal intensity ratio of 8 was achieved at

50mm spatial offset was. This value was 4.5 times greater that achieved by the TRRS

at 50 ns gate delay. The observed difference in the signal intensity ratio can be

attributed in part to the large offset implemented for the SORS measurement (50

mm).


Figure 5.18: SORS analysis of ammonium nitrate in a yellow polystyrene container


Figure 5.19: Signal intensity ratio as a function of spatial offsets for the SORS analysis of ammonium


The SNR plot (Figure 5.20) indicated a similar trend to that indicated by Maher

and Berger [135] where the SNR reaches a peak before it starts to decrease again.

The results indicate that a SORS spectrum that is a representative of the ammonium

nitrate content alone can be achieved by implementing a proper spatial offset.

However the SNR may become very low at very large spatial offsets (as indicated by

the low SNR value at 50 mm offset in fig 5.20). Therefore the SNR should be taken

into account when determining the best offset to acquire a SORS spectrum of the

chemical content alone.

Figure 5.20: Signal-to-noise ratio as a function of spatial offsets for the SORS analysis of ammonium



A scaled subtraction performed between a spectrum obtained at zero offset and

a spectrum obtained at a 20 mm offset is demonstrated in figure 5.21. The resulting

scaled-subtracted spectrum shows a spectral profile that is characteristic of

ammonium nitrate alone which indicate that scaled subtraction can be sufficient for

retrieval of the concealed substance spectrum. The analysis was repeated at 8m. The

resulting spectrum is presented in figure 5.22.

Figure 5.21: Demonstration of a scaled subtraction between two spectra obtained at different offsets

for ammonium nitrate concealed in a yellow polystyrene container

Figure 5.22: SORS spectrum of ammonium nitrate concealed in a yellow polystyrene container at 8

metres. A scaled subtraction between spectra obtained at zero offset and a 15mm offset was carried

out.



Both sets of results have indicated the enhanced efficiency of depth profiling

that is achieved by SORS in comparison to TRRS. An exponential increase in the

signal intensity ratio was observed within the SORS measurements. Results from the

yellow container indicated an increase in the SNR to a maximum before it decrease

again at large spatial offset values. Therefore applying a large spatial in an attempt to

acquire a SORS spectrum from the deep layers of a sample may be hindered by the

very low signal to noise ratio at the optimum spatial offset. Instead, scaled

subtraction of SORS spectra that are acquired at spatial offsets other than the ideal

offset can be applied to obtain a SORS spectrum that is a representative of the deep

layers of a sample alone.

5.4.2 Standoff SORS Detection at 3 meters

A 2mm thick white opaque HDPE container that was utilised accommodated a

maximum offset of 15mm. Samples utilised for this study included aspirin, 2,2-

thiodiethanol, GBL (Gama hydroxyl butyl lactone) as well as 30% v/v hydrogen

peroxide. As such, two measurements were procured; one from a zero offset and one

from an offset of 15mm. A scaled subtraction was performed on each of the resulting

spectrum. The spectra as well as their respective subtractions are listed in figures

5.23 – 5.26. For every sample, a difference was notable in terms of the relative

Raman signal contributions from the container and the sample between the zero and

15mm offset. A clean spectrum was procured for the concealed aspirin upon

subtraction. As for the three liquids, spectral artefacts are observed as a result of the

scaled subtractions. This is a limitation of performing scaled subtractions. However,

identification is possible based on the prominent peaks within the subtracted spectra.


Figure 5.23: SORS analysis of aspirin in a white HDPE container Figure 5.24: SORS analysis of 2,2-thiodiethanol in a white HDPE container


Figure 5.25: SORS analysis of GBL in a white HDPE container Figure 5.26: SORS analysis of hydrogen peroxide in a white HDPE

container


5.4.3 Stand-off SORS Detection at 15 metres

In order to observe the SORS effect from a working distance of 15m, varying

offsets were implemented on the explosive precursors concealed within a 1.5mm

thick HDPE container being utilised. The resulting spectra are listed in figures 5.27-

5.29. In all three cases, the same effect was observed where a significant degree of

the Raman signal from the container was suppressed.

Figure 5.27: SORS analysis of 2,4-DNT in a white HDPE container


Figure 5.28: SORS analysis of nitromethane in a white HDPE container Figure 5.29: SORS analysis of ammonium nitrate in a white HDPE container


5.5 CONCLUSION

The concept of SORS was introduced and the effect of SORS was

demonstrated utilising a CW and pulsed wave SORS configurations. Continuous

wave (CW) SORS was utilised for the detection as well as the semi-quantitative

analysis of concealed substances. Our results prove that the PCA plots are capable of

distinguishing mixtures of differing entities. Bearing in mind that PCA is an

unsupervised learning technique, this qualitative analysis can be further enhanced by

adopting a supervised learning technique such as SIMCA and K Nearest Number

(KNN) for the purpose of establishing a rapid classification system. CW SORS was

demonstrated within a close working range of 6cm. The use of a NIR laser excitation

facilitated the detection of substances that were concealed in highly fluorescing

coloured packaging. A notable advantage of SORS, aside from its ability to provide a

spectrum of a concealed substance, is that the low signal to noise level within a

SORS spectrum. Therefore it can be used directly into a PCA algorithm without a

need for a smoothing procedure. Results from the PLS analysis indicated a 0.6%

deviation from the true results. However, this deviation is not significant especially

when the investigated substance is concealed in an opaque packaging. SORS in

combination with multivariate statistical techniques can be adapted to the detection

any semi- quantitative analysis of drugs of abuse or counterfeit pharmaceutical

products where the identity and concentrations are of interest. CW SORS under

background lightning conditions was demonstrated for the first time for samples that

were concealed in coloured and non-coloured packaging. The ability to carry out

SORS detection under incandescent, fluorescent and day light background

illumination confirmed the applicability of the technique for real life measurements

in the field.

Pulsed SPORS was utilized for the standoff detection of concealed substances

at different offset distances of 3, 8 and 15 meters. Pulsed laser excitation and fast

gated ICCD detection were utilised for the standoff SORS detection. Standoff SORS

was demonstrated for the first time for the identification of substances that are

concealed in highly fluorescing coloured packaging. The results confirmed that

standoff SORS has higher selectivity towards the deep layers of a sample when

compared to that of standoff TRRS. This is confirmed by the high signal intensity

ratio observed in the standoff detection by SORS.

Chapter 6: Time-Resolved Spatially Offset Raman Spectroscopy 102

Chapter 6: Time-Resolved Spatially Offset Raman

Spectroscopy

6.1 INTRODUCTION

The concepts of TRRS and SORS have been comprehensively detailed in the

previous chapters. TRRS has the capability of selectively detecting the Raman

photons arising from the deeper layers by temporally shifting the detector gate while

SORS spatially discriminates against photons that arise from the surface layer by

implementing a lateral spatial offset between the excited spot and the Raman

collection system onto the surface of the sample. By integrating both concepts, a

synergistic effect would result in an enhanced selectivity towards the deeper layers of

a sample as well as enhanced fluorescence and background noise rejection. The

combination of TRRS and SORS techniques has the prospect of further facilitating

the non-invasive depth profiling of concealed substances. Both TRRS and SORS are

applicable to pulsed wave configurations, as demonstrated in the previous chapters,

which facilitates the amalgamation of the two techniques.

Petterson et al demonstrated the enhanced selectivity of time–resolved

spatially-offset Raman spectroscopy (TR-SORS) technique towards the deeper layer

of a sample as well as the enhanced fluorescence rejection capability relative to a

conventional SORS technique [131]. However, the use of a picosecond-scale system

and a significantly narrow gate width was detrimental to the SNR of the resulting

spectral data obtained. Furthermore, the instrumental configuration that was utilised

was relatively complex.


6.2 AIMS

- To provide an illustrated discussion on the compounded effects of SORS and

TRRS which is the very core of the TR-SORS effect

- To integrate TRRS and SORS whilst utilising a nanosecond-scale configuration

in an effort to overcome the restricted signal-to-noise ratio achieved in preceding

studies.

- To apply the TR-SORS configuration to both close-range and stand-off detection

modes.

- To investigate the efficiency of TR-SORS in terms of selectivity and signal-to-

noise ratio and to compare it with TRRS and SORS.

- To attempt the integration of a near-infrared (NIR) illumination source for the

detection of samples concealed within fluorescing coloured packaging materials

at a working distance of 6cm.

6.3 CONCEPT OF TR-SORS

Although TR-SORS has been demonstrated once by Petterson et al, the concept

behind it has not been detailed. As such, this dissertation aims to provide an

illustrated discussion on the compounded effects of SORS and TRRS which is the

very core of the TR-SORS effect. Chapter 5 demonstrated that by implementing a

spatial offset between the points of excitation and Raman photon collection, a lower

proportion of the Raman photons from the surface are detected relative to those

detected at a zero offset. Additionally, due to the broader intensity profile exhibited

by Raman photons from the deeper layer, implementing an offset increases the

detection ratio of Raman photons from the deeper layer to that of the surface layer.

Chapter 4 demonstrated the resulting temporal profiles of the Raman photons from

the surface and deep layer of a sample upon excitation by a laser [fig 4.2]. Temporal

resolution of the deep layers Raman photons requires the synchronization of the

detector gate in time with the arrival of the deep layer Raman photons as illustrated

in section 4.3.

Figure 6.1 illustrates how the combined effects of time and space resolution

can lead to the detection of a higher proportion of the deep layer Raman photons and,


in effect, the higher selectivity of TR-SORS towards the deep layers of a sample. The

diagram illustrated shows the surface layer which represents a container material.

Three laser excitation points are indicated on the surface. The position of the first

laser excitation point on the left coincides with the Raman photon collection point.

This is akin to a zero offset detection where the acquired signal would exhibit

significant contributions from the surface layer and only low contributions from the

deep layer. When the detector gate is shifted to a proper position in time (i.e.

synchronising the detector gate delay with the arrival of the delayed deep layer

Raman photons), as in TRRS, temporal resolution between the deep layer Raman

photons and the surface layer photons is achieved and the deep layer Raman photons

are selectively detected over the surface layer Raman photons (Figure 6.1a).

However, at this point in time (after T5), due to the broad distribution of the deep

layer Raman photons, a low proportion of the deep layer Raman photons would be

detected by the gated detector. This causes the low SNR in TRRS measurements. By

positioning the laser at a spatial offset a significantly lower count of the surface layer

photons is detected by the gated detector in comparison to that detected from a zero

offset. This is characteristic of SORS. In effect, by positioning the laser at a spatial

offset of ΔS1, a higher proportion of the deeper layer Raman photons can be detected

relative to that of the surface layer photons as demonstrated in figure 6.1b. The larger

the spatial offset, the higher the proportion of the deep layer Raman photons that can

be detected by the detector as indicated by figure 6.1c where a relatively large offset

of ΔS2 is implemented. At this point in space, shifting the gate delay in time would

result in a greater discrimination against the surface layer photons and higher

selectivity towards the deeper layer Raman photons that are beyond the individual

capability of TRSS or SORS. The above explanations are further tested and

confirmed through the following TR-SORS experiments.


Figure 6.1: Effect of spatial offsets on the temporal profile of resulting Raman photons

6.4 STAND-OFF TR-SORS DETECTION STUDY

6.4.1 Preliminary Analysis

A comprehensive analysis was conducted to investigate the efficiency of TR-

SORS in comparison to TRRS and SORS in terms of its selectivity for the deeper

layer. Ammonium nitrate was concealed in a white opaque 2mm thick HDPE

container as well as a yellow opaque 2mm thick polystyrene container and screened

from a stand-off distance of 3 meters.

Ammonium Nitrate Concealed in White Plastic Container

For each sample measurement, a spatial offset was first introduced by shifting

the illumination point horizontally relative to the collection point. The gated

detection system was synchronised at 41ns where the maximum Raman signal

intensity was detected. Subsequent gate delays of 1ns were then implemented such

that the detection gate was temporally shifted in steps of 1ns throughout the analysis.

The TR-SORS measurements were repeated at spatial offsets of 5, 10, 15, 20 and 25


mm. At each spatial offset, the detector gate was progressively shifted in time using

increments of 1 nanosecond. At each offset, spectral measurements were acquired at

gate delays of 41ns, 44ns, 47ns and 50ns.

The resulting spectra are listed in figures 6.2 – 6.6. The change in the signal

intensity ratio with the change in the detector gate delay at each spatial offset is

presented in figure 6.7. As indicated by the figure, the signal intensity ratio increased

exponentially with the increase in the detector gate delay. The figure also indicated

that the signal intensity ratio increased when the spatial offset was increased. It is

useful to note that the signal intensity ratio achieved by SORS for the detection of

ammonium nitrate in the same white HDPE container from a standoff distance of 3

meters was 10.5 at a 25 mm spatial offset (Figure 5.16). A similar signal intensity

ratio was achieved by implementing a spatial offset of 10mm and a detector gate

delay of 50ns in the TR-SORS measurement. A dramatic increase in the signal

intensity ratio was observed when TR-SORS detection was carried out at 25 mm

offset with a detector gate delay of 50 ns where the signal intensity ratio reached a

value of 68. Therefore the signal intensity ratio in the TR-SORS measurement was

6.5 times higher than that in the SORS measurement (Figure 5.16) and 8.5 higher

than that in the TRRS (Figure 4.5) measurement of the same sample at the same

stand-off distance. This significant increase in the signal intensity ratio confirmed the

higher selectivity of the TR-SORS technique towards the deep layers of a sample

relative to SORS and TRRS alone.

The trend observed in the SNR plot (Figure 6.8) is congruent with that of

TRRS where the SNR eventually decreases with increasing gate delays (Figure 4.6).

The phenomenon behind this trend has been detailed in section 4.3. However, in

contrast to TRRS, TR-SORS demonstrated a higher SNR. On the other hand the SNR

in the SORS measurement at 25 mm offset (Figure 5.17) was relatively higher than

that observed in TR-SORS at the same offset (fig 6.8). Therefore TR-SORS exhibits

a highest selectivity towards the deeper layers of a sample, in comparison to the other

2 techniques, while maintaining the signal to noise ratio in the acquired spectrum at

an acceptable level.




Figure 6.7: Signal intensity ratio for the TR-SORS analysis of

ammonium nitrate in a white HDPE container

Figure 6.8: Signal-to-noise ratio for the TR-SORS analysis of

ammonium nitrate in a white HDPE container


Ammonium Nitrate in Yellow Container

Beginning with a 10mm offset, measurements were conducted up to a

maximum offset of 50mm with 10mm increments. Spectral measurements were

conducted at 41ns, 44ns, 47ns and 50ns. The resulting spectra are listed in figures 6.9

– 6.13. The synergistic effect of spatial and temporal resolution in TR-SORS analysis

of ammonium nitrate is clearly noticeable throughout the spectra. Additionally, a TR-

SORS spectrum that represents the ammonium nitrate content alone was achieved at

an offset of 30mm without the need to carry out a scaled-subtraction, unlike TRRS

and SORS.

The trends observed in the signal intensity plot (Figure 6.14) are congruent

with the findings obtained from the initial analysis where an increasingly exponential

increase in the ratio is observed as increasing offsets are utilised. However, the trend

observed within the SNR plot (Figure 6.15) is in stark contrast to that observed for

the initial analysis. This is attributed to the SORS effect on the SNR trend which has

been elaborated on in section 5.1.1 where the SNR increases to a peak at 30mm

offset before decreasing. As a result, the plot in figure 6.15 indicated that the SNR

trends throughout the 40mm and 50 mm offsets are comparatively lower than that of

a 30mm offset. However, the spectral results attained exhibited an enhanced SNR in

comparison to TRRS. To confirm the applicability and reproducibility of TR-SORS

for the standoff detection of concealed substances in highly fluorescing coloured

packaging materials, the TR-SORS detection of ammonium nitrate in the yellow

container was repeated at a distance of 8 metres. The resulting spectrum is presented

in figure 6.16. A scaled subtraction was not required in this case to retrieve a

spectrum of ammonium nitrate.




Figure 6.14: Signal intensity ratio for the TR-SORS analysis of

ammonium nitrate in a yellow polystyrene container

Figure 6.15: Signal-to-noise ratio for the TR-SORS analysis of

ammonium nitrate in a yellow polystyrene container


Figure 6.16: TR-SORS spectrum of ammonium nitrate concealed in a yellow polystyrene

container detected from 8 metres. The measurement was carried out at a spatial offset of

15mm and a gate delay of 86ns


The compounded effects of spatial and temporal resolution were demonstrated

in two scenarios. In both cases, similar trends were observed in the signal intensity

ratio. A better understanding of the SNR trend was achieved when the yellow

polystyrene container was utilised due to the accommodation of larger offsets. The

results reiterate the behaviour exhibited by SORS. In both cases, the SNR within the

TR-SORS measurements was enhanced relative to TRRS. However, in comparison

to SORS, when additional gate delays were implemented, a gradual decrease in the

SNR was observed. Despite the difference in behaviour in terms of SNR, the trends

in the signal intensity plot has indicated the enhanced selectivity exhibited by TR-

SORS for the deeper layer relative to both TRRS and SORS.


6.4.2 Stand-off TR-SORS Detection at 3 metres

The results for the TR-SORS analysis on subsequent samples concealed in a

white opaque 2mm thick container are listed in figures 6.17 – 6.20. A 15mm offset

was imposed followed by the implementation of gate delays which enhanced the

signal intensity ratio such that a TR-SORS spectrum of the deep layer of each sample

was successfully obtained without the need to use any sophisticated algorithms for

spectral data treatments. This is in stark contrast to TRRS and SORS where a scaled

subtraction was required in order to recover a spectral profile of the concealed

substance alone. In the case of hydrogen peroxide, however, minor contributions

from the container were still observed. This could be attributed to the low Raman

cross section of the sample as well as the fact that it has been significantly diluted.

However, the identification of the concealed hydrogen peroxide solution was still

possible.


Figure 6.17: TR-SORS analysis of aspirin in a white HDPE container at

a 15mm spatial offset

Figure 6.18: TR-SORS analysis of 2,2-thiodiethanol in a white HDPE



Figure 6.19: TR-SORS analysis of GBL in a white HDPE container at a

15mm spatial offset

Figure 6.20: TR-SORS analysis of hydrogen peroxide in a white HDPE



6.4.3 Stand-off TR-SORS Detection at 15 metres

Standoff TR-SORS detection of 2,4-DNT, ammonium nitrate and nitromethane

was carried out at 15 meters. The analytes were concealed in a 1.5mm thick white

opaque HDPE container and an offset of 15mm was utilized. The acquired TR-SORS

spectra are presented in figure 6.21a-c. By shifting the detector gate delay in time, an

enhanced suppression of the surface layer Raman signals was observed. In all three

cases, TR-SORS was able to efficiently provide a spectrum of the concealed

substance alone with no residual contribution from the container. These tests

demonstrated the efficiency of TR-SORS in the interrogation of concealed

substances at a working distance of 15m. The acquired TR-SORS spectra did not

show any residual Raman signal from the HDPE polymer. This study confirmed the

feasibility of utilising TR-SORS to identify explosive precursors concealed in

diffusely scattering packaging materials.


Figure 6.21: TR-SORS spectra of (a) 2,4-DNT, (b) ammonium nitrate and (c) nitromethane concealed

in a white HDPE container


6.5 TR-SORS DETECTION AT 6CM

Preliminary analyses in the stand-off TR-SORS analysis as well as the

subsequent analysis on a range of different samples have demonstrated the enhanced

selectivity of TR-SORS in comparison to TRRS and SORS. Clean spectra of

concealed samples from distances up to 15m were achieved without the need for

further treatments. In this section, a non-contact distance of 6cm is attempted for TR-

SORS by utilising an inverse-SORS mode of illumination. As indicated in the

introduction of SORS (Section 5.1.2), studies have indicated the advantages of an

inverse-SORS illumination mode over a conventional SORS illumination mode

[139]. As such, an inverse-SORS illumination mode was adopted within this

configuration in which an annular illumination of 14mm diameter (spatial offset of

7mm) was utilised for the samples analysed throughout this section. A near-infrared

(NIR) laser illumination at 785 nm was utilised. Packaging materials such as

coloured HDPE containers have been utilised to study the tolerance of the

configuration. The instrumental configuration for this system has been detailed in

section 3.1.3. The spectra throughout the analysis were obtained at a gate delay of

50ns and at an average measurement time of 50 seconds (100 pulses, 5 acquisitions).


6.5.1 TR-SORS Detection of Samples Concealed in Non-coloured Packaging

Materials

Figure 6.22: TR-SORS spectra of a) ammonium nitrate, (b) nitromethane and (c) hydrogen peroxide

concealed in non-coloured containers

The resulting spectra of the substances concealed within various types of

packaging indicated no Raman signal contribution from the container whilst

providing a good match with the reference spectra.


6.5.2 TR-SORS Detection of Samples Concealed in Coloured Packaging

Materials

Figure 6.23: TR-SORS spectra of (a) ammonium nitrate (b) ammonium nitrate (c) 2,4-DNT and (d)

hydrogen peroxide in different coloured containers

The TR-SORS detection of ammonium nitrate, 2,4-DNT and hydrogen

peroxide solution (30% w/w) was repeated in coloured HDPE packaging. The results

are presented in figure 6.23. All of the acquired TR-SORS measurements did not

show any spectral contributions from the HDPE material. In the case of ammonium

nitrate concealed within the blue and purple HDPE containers, there was no

significant interfering background noise caused by fluorescence from the containers.

However, the red HDPE container contributed much fluorescence that resulted in a

significant sloping baseline. However, all the respective signals belonging to 2,4-

DNT and hydrogen peroxide were present in the spectra. In cases such as these, a

baseline correction would suffice in rectifying the sloping baseline. These testes

further confirmed the capability of TR-SORS to detect concealed substances in

highly fluorescing packaging


6.6 CONCLUSION

This chapter has demonstrated the applicability of a nanosecond-scale system

to a TR-SORS spectrometer. TR-SORS was demonstrated for the first time for the on

concealed chemical substances in highly fluorescing coloured packaging at the

working distances of TR-SORS ranging from 6cm to 15m. The use of NIR excitation

was proven to facilitate the retrieval of the deep layer Raman photons of a sample

despite fluorescence arising from the coloured packaging materials. The signal

intensity ratio exhibited by TR-SORS was found to be superior to that of TRRS and

SORS. Utilising TR-SORS improved the SNR in comparison to TRRS due to the

implementation of an offset which increases the proportion of Raman photons from

the deeper layer being detected. Additionally, the resulting spectra presented

throughout this chapter did not require any scaled subtraction to retrieve a spectrum

that is characteristic of the deeper layer.

Chapter 6: Summary 124

Chapter 7: Summary

7.1 CONCLUSIONS

Deep Raman spectroscopy is a recently developed field that facilitates the

interrogation of deeper layers of a sample. Three main techniques within Deep

Raman Spectroscopy have been introduced throughout this dissertation; TRRS,

SORS and TR-SORS. All three techniques have been demonstrated to be adaptable

to nanosecond-scale configuration which facilitates a higher SNR in comparison to a

picosecond-scale system. Additionally, each technique was successfully utilised for

the stand-off detection of concealed substances from up to 15m.

The techniques were subjected to a preliminary analysis in which the efficiency

and the quality of the resulting spectra were investigated. The efficiency and quality

of these techniques were determined based on their selectivity towards the deeper

layers of a sample by investigating the signal intensity ratio as well as the SNR of the

resulting spectra. Results procured throughout the research indicate that TR-SORS

exhibits the highest selectivity for the deeper layer. This is attributed to the combined

effect of spatial and temporal resolution of the Raman photons arising from the

deeper layer. TRRS exhibited the lowest selectivity towards the deep layers of a

sample among the three techniques due to the low photon count at which a spectrum

that has minimal spectral contribution from the container may be achieved. In terms

of the SNR, SORS exhibits a trend in which the SNR increases to a maximum with

increasing spatial offsets before gradually decreasing. Among the three techniques,

SORS is capable of attaining the highest SNR followed by TR-SORS and TRRS in

that order.

These results indicate that TR-SORS is superior to TRRS and SORS as an

efficient technique for the depth profiling of concealed substances due to its

enhanced selectivity for the deeper layer as well as the provision of spectra with

good SNR. Additionally, scaled subtractions are not necessary to retrieve a spectrum

of the deeper layer. TR-SORS may be applied to non-coloured and coloured

packaging. The effect of utilising a NIR illumination source has been demonstrated

in the close range TR-SORS analysis for avoiding significant fluorescence from

coloured packaging and, therefore, the successful recovery of spectral profiles


characteristic of only the deep layers of the sample. A feasibility study on the use of

chemometric techniques on the spectra acquired from CW SORS was conducted with

positive results. Despite the use of scaled subtractions prior to the chemometric

applications, the results indicated the ability of such techniques to qualitatively and

quantitatively analyse the concealed samples non-invasively.

7.2 RECOMMENDATIONS FOR FURTHER RESEARCH

Throughout this research, the concepts on each of the techniques were

highlighted, specifically elaborating on the temporal and spatial profiles of Raman

photons. In order to better comprehend the resulting profiles, especially when dealing

with concealed items; it would be useful to investigate the effects of detecting

packaging with differing thickness and packing density on the resulting temporal and

spatial profiles of the Raman photons from the surface and deeper layers.

With the successful demonstration of the application of chemometric

techniques in SORS, application of more advanced supervised learning techniques to

the resulting TR-SORS spectra may lead to an efficient and rapid non-invasive

classification and quantification system. As such, unsupervised and supervised

techniques may be investigated on spectra retrieved from TR-SORS to determine the

accuracy in providing qualitative and semi-quantitative information on concealed

sample.


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