DEEP RAMAN SPECTROSCOPY IN THE ANALYTICAL ......5. Izake E, Sundarajoo S, Olds W, Cletus B, Jaatinen...
Transcript of 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
Chapter 1: Introduction 2
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.
Chapter 1: Introduction 3
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
Chapter 1: Introduction 4
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
Chapter 1: Introduction 5
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
Chapter 1: Introduction 6
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
Chapter 1: Introduction 7
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)
Chapter 1: Introduction 8
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
Figure 5.15: Demonstration of a scaled subtraction between two spectra
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
Figure 5.21: Demonstration of a scaled subtraction between two spectra
obtained at different offsets for ammonium nitrate concealed in a yellow
polystyrene container
Chapter 1: Introduction 9
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 6.3: TR-SORS analysis of ammonium nitrate in a white HDPE
container at a 10mm spatial offset
Figure 6.4: TR-SORS analysis of ammonium nitrate in a white HDPE
container at a 15mm spatial offset
Figure 6.5: TR-SORS analysis of ammonium nitrate in a white container at
a 20mm spatial offset
Figure 6.6: TR-SORS analysis of ammonium nitrate in a white HDPE
container at a 25mm 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 6.10: TR-SORS analysis of ammonium nitrate in a yellow
polystyrene container at a 20mm spatial offset
Chapter 1: Introduction 10
Figure 6.11: TR-SORS analysis of ammonium nitrate in a yellow
polystyrene container at a 30mm spatial offset
Figure 6.12: TR-SORS analysis of ammonium nitrate in a yellow
polystyrene container at a 40mm spatial offset
Figure 6.13: TR-SORS analysis of ammonium nitrate in a yellow
polystyrene container at a 50mm 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
container at a 15mm spatial offset
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
container at a 15mm spatial offset
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
Chapter 1: Introduction 11
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
Chapter 1: Introduction 12
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
Chapter 1: Introduction 13
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: _________________________
Chapter 1: Introduction 14
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 15
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.
Chapter 1: Introduction 16
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.
Chapter 1: Introduction 17
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
Chapter 1: Introduction 18
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.
Chapter 4: Literature Review 21
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.
Chapter 4: Literature Review 22
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
Chapter 4: Literature Review 23
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
Chapter 4: Literature Review 24
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].
Chapter 4: Literature Review 25
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
Chapter 4: Literature Review 26
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
Chapter 4: Literature Review 27
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.
Chapter 4: Literature Review 28
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
Chapter 4: Literature Review 29
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
Chapter 4: Literature Review 30
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
Chapter 4: Literature Review 31
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.
Chapter 4: Literature Review 32
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
Chapter 4: Literature Review 33
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.
Chapter 4: Experimental Design 35
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.
Chapter 4: Experimental Design 36
Figure 3.2: Stand-off detection performed at (a) 3m, (b) 8m and (c) 15m
Chapter 4: Experimental Design 37
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
Chapter 4: Experimental Design 38
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
Chapter 4: Experimental Design 39
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
Chapter 4: Experimental Design 40
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
Chapter 4: Experimental Design 41
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
Chapter 4: Experimental Design 42
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
Chapter 4: Time-Resolved Raman Spectroscopy 44
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.
Chapter 4: Time-Resolved Raman Spectroscopy 45
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).
Chapter 4: Time-Resolved Raman Spectroscopy 46
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].
Chapter 4: Time-Resolved Raman Spectroscopy 47
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
Chapter 4: Time-Resolved Raman Spectroscopy 48
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
Chapter 4: Time-Resolved Raman Spectroscopy 49
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]
Chapter 4: Time-Resolved Raman Spectroscopy 50
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.
Chapter 4: Time-Resolved Raman Spectroscopy 51
Figure 4.4: TRRS of ammonium nitrate in a white HDPE container
Chapter 4: Time-Resolved Raman Spectroscopy 52
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
nitrate concealed in a white HDPE container
Chapter 4: Time-Resolved Raman Spectroscopy 53
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
Chapter 4: Time-Resolved Raman Spectroscopy 54
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
Chapter 4: Time-Resolved Raman Spectroscopy 55
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 intensity ratio as a function of gate delays for the TRRS analysis of ammonium
nitrate concealed in a yellow polystyrene container
Chapter 4: Time-Resolved Raman Spectroscopy 56
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.
Chapter 4: Time-Resolved Raman Spectroscopy 57
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
Chapter 4: Time-Resolved Raman Spectroscopy 59
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
Chapter 4: Time-Resolved Raman Spectroscopy 60
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
Chapter 4: Time-Resolved Raman Spectroscopy 61
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
Chapter 4: Time-Resolved Raman Spectroscopy 62
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].
Chapter 5: Spatially-Offset Raman Spectroscopy 65
Figure 5.1: Illustration of the spatial effects of Raman photons undergoing diffused scattering in a two
layered diffusely scattering medium
Chapter 5: Spatially-Offset Raman Spectroscopy 66
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
Chapter 5: Spatially-Offset Raman Spectroscopy 67
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,
Chapter 5: Spatially-Offset Raman Spectroscopy 68
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.
Chapter 5: Spatially-Offset Raman Spectroscopy 69
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
Chapter 5: Spatially-Offset Raman Spectroscopy 70
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
Chapter 5: Spatially-Offset Raman Spectroscopy 71
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.
Chapter 5: Spatially-Offset Raman Spectroscopy 72
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
Chapter 5: Spatially-Offset Raman Spectroscopy 73
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)
Chapter 5: Spatially-Offset Raman Spectroscopy 74
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
Chapter 5: Spatially-Offset Raman Spectroscopy 75
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
Chapter 5: Spatially-Offset Raman Spectroscopy 76
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)
Chapter 5: Spatially-Offset Raman Spectroscopy 77
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.
Chapter 5: Spatially-Offset Raman Spectroscopy 78
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-
Chapter 5: Spatially-Offset Raman Spectroscopy 79
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.
Chapter 5: Spatially-Offset Raman Spectroscopy 80
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.
Chapter 5: Spatially-Offset Raman Spectroscopy 81
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.
Chapter 5: Spatially-Offset Raman Spectroscopy 82
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.
Chapter 5: Spatially-Offset Raman Spectroscopy 83
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.
Chapter 5: Spatially-Offset Raman Spectroscopy 84
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.
Chapter 5: Spatially-Offset Raman Spectroscopy 85
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
Chapter 5: Spatially-Offset Raman Spectroscopy 86
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
Chapter 5: Spatially-Offset Raman Spectroscopy 87
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.
Chapter 5: Spatially-Offset Raman Spectroscopy 88
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.
Chapter 5: Spatially-Offset Raman Spectroscopy 89
Figure 5.14: SORS analysis of ammonium nitrate in a white HDPE container
Chapter 5: Spatially-Offset Raman Spectroscopy 90
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.
Chapter 5: Spatially-Offset Raman Spectroscopy 91
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
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
Chapter 5: Spatially-Offset Raman Spectroscopy 92
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).
Chapter 5: Spatially-Offset Raman Spectroscopy 93
Figure 5.18: SORS analysis of ammonium nitrate in a yellow polystyrene container
Chapter 5: Spatially-Offset Raman Spectroscopy 94
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
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
nitrate concealed in a yellow polystyrene container
Chapter 5: Spatially-Offset Raman Spectroscopy 95
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.
Chapter 5: Spatially-Offset Raman Spectroscopy 96
Summary of Preliminary Analysis
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.
Chapter 5: Spatially-Offset Raman Spectroscopy 97
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
Chapter 5: Spatially-Offset Raman Spectroscopy 98
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
Chapter 6: Spatially-Offset Raman Spectroscopy 99
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
Chapter 6: Spatially-Offset Raman Spectroscopy 100
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
Chapter 6: Spatially-Offset Raman Spectroscopy 101
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.
Chapter 6: Time-Resolved Spatially Offset Raman Spectroscopy 103
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,
Chapter 6: Time-Resolved Spatially Offset Raman Spectroscopy 104
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.
Chapter 6: Time-Resolved Spatially Offset Raman Spectroscopy 105
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
Chapter 6: Time-Resolved Spatially Offset Raman Spectroscopy 106
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.
Chapter 6: Time-Resolved Spatially Offset Raman Spectroscopy 107
Figure 6.2: TR-SORS analysis of ammonium nitrate in a white HDPE
container at a 5mm spatial offset
Figure 6.3: TR-SORS analysis of ammonium nitrate in a white HDPE
container at a 10mm spatial offset
Chapter 6: Time-Resolved Spatially Offset Raman Spectroscopy 108
Figure 6.4: TR-SORS analysis of ammonium nitrate in a white HDPE
container at a 15mm spatial offset
Figure 6.5: TR-SORS analysis of ammonium nitrate in a white HDPE
container at a 20mm spatial offset
Chapter 6: Time-Resolved Spatially Offset Raman Spectroscopy 109
Figure 6.6: TR-SORS analysis of ammonium nitrate in a white HDPE
container at a 25mm 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
Chapter 6: Time-Resolved Spatially Offset Raman Spectroscopy 110
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.
Chapter 6: Time-Resolved Spatially Offset Raman Spectroscopy 111
Figure 6.9: TR-SORS analysis of ammonium nitrate in a yellow
polystyrene container at a 10mm spatial offset
Figure 6.10: TR-SORS analysis of ammonium nitrate in a yellow
polystyrene container at a 20mm spatial offset
Chapter 6: Time-Resolved Spatially Offset Raman Spectroscopy 112
Figure 6.11: TR-SORS analysis of ammonium nitrate in a yellow
polystyrene container at a 30mm spatial offset
Figure 6.12: TR-SORS analysis of ammonium nitrate in a yellow
polystyrene container at a 40mm spatial offset
Chapter 6: Time-Resolved Spatially Offset Raman Spectroscopy 113
Figure 6.13: TR-SORS analysis of ammonium nitrate in a yellow
polystyrene container at a 50mm 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
Chapter 6: Time-Resolved Spatially Offset Raman Spectroscopy 114
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
Summary of Preliminary Analysis
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.
Chapter 6: Time-Resolved Spatially Offset Raman Spectroscopy 115
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.
Chapter 6: Time-Resolved Spatially Offset Raman Spectroscopy 116
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
container at a 15mm spatial offset
Chapter 6: Time-Resolved Spatially Offset Raman Spectroscopy 117
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
container at a 15mm spatial offset
Chapter 6: Time-Resolved Spatially Offset Raman Spectroscopy 118
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.
Chapter 6: Time-Resolved Spatially Offset Raman Spectroscopy 119
Figure 6.21: TR-SORS spectra of (a) 2,4-DNT, (b) ammonium nitrate and (c) nitromethane concealed
in a white HDPE container
Chapter 6: Time-Resolved Spatially Offset Raman Spectroscopy 120
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).
Chapter 6: Time-Resolved Spatially Offset Raman Spectroscopy 121
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.
Chapter 6: Time-Resolved Spatially Offset Raman Spectroscopy 122
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
Chapter 6: Time-Resolved Spatially Offset Raman Spectroscopy 123
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
Chapter 6: Summary 125
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.
Chapter 6: Summary 126
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