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Machine Learning Methods for Quantitative Analysis of Raman
Spectroscopy Data
Michael G. Madden*
a and Alan G. Ryder
b
a Department of Information Technology, NUI-Galway, Ireland.
b Department of Physics, NUI-Galway, Ireland.
ABSTRACT
The automated identification and quantification of illicit materials using Raman spectroscopy is of significant importance
for law enforcement agencies. This paper explores the use of Machine Learning (ML) methods in comparison with standard
statistical regression techniques for developing automated identification methods. In this work, the ML task is broken into
two sub-tasks, data reduction and prediction. In well-conditioned data, the number of samples should be much larger than
the number of attributes per sample, to limit the degrees of freedom in predictive models. In this spectroscopy data, the
opposite is normally true. Predictive models based on such data have a high number of degrees of freedom, which increases
the risk of models over-fitting to the sample data and having poor predictive power. In the work described here, an approach
to data reduction based on Genetic Algorithms is described. For the prediction sub-task, the objective is to estimate the
concentration of a component in a mixture, based on its Raman spectrum and the known concentrations of previously seen
mixtures. Here, Neural Networks and k-Nearest Neighbours are used for prediction. Preliminary results are presented for the
problem of estimating the concentration of cocaine in solid mixtures, and compared with previously published results in
which statistical analysis of the same dataset was performed. Finally, this paper demonstrates how more accurate results
may be achieved by using an ensemble of prediction techniques.
Keywords: Forensic science; Narcotics; Regression; Raman; Spectroscopy; Machine Learning; Ensemble; Genetic
Algorithm; Neural Network.
1. INTRODUCTION
Raman spectroscopy is being used in forensic science research for the identification and analysis of narcotics, explosives,
polymers, and other materials.
1 Raman spectra are unique and are based on the vibrational motions of molecules, which
provides a chemical fingerprint suitable for identification and discrimination of a wide range of materials.
2 Furthermore, the
development of fiber optic Raman probes will allow the implementation of portable devices for in-situ examination of
suspect materials including narcotic.
3
Examples of illicit narcotics analysed by Raman spectroscopy in the laboratory
include cocaine,
4 heroin,
5 and amphetamines in both solid and solution.
6, 7 In reality, however, the composition of seized
drug samples can very enormously and it is unlikely that suspect materials will contain only one or two pure diluents. The
vast range of possible diluents and impurities that may be present pose several problems for the use of Raman spectroscopy
for the quantitative and qualitative analysis of illicit drugs. Difficulties include the presence of fluorescent materials, which
obscure Raman peaks, overlap of diluent with narcotic Raman peaks, and variations in signal quality.
To help overcome these problems many investigators a have turned to advanced computational methods to improve the
quantitative and qualitative capability of Raman spectroscopy. For quantitative measurements, the use of chemometrics
(multivariate analysis) has been demonstrated in the prediction of fuel composition,
8 metabolites in urine,
9 and cocaine.
10, 11
In our previous work, we have employed traditional chemometric methods (Partial Least Squares) for the development of
quantitative models for predicting cocaine concentration in solid mixtures.
10, 11 Unfortunately, as the mixtures become more
Machine Learning Methods for Quantitative Analysis of Raman
Spectroscopy Data
Michael G. Madden*
a and Alan G. Ryder
b
a Department of Information Technology, NUI-Galway, Ireland.
b Department of Physics, NUI-Galway, Ireland.
ABSTRACT
The automated identification and quantification of illicit materials using Raman spectroscopy is of significant importance
for law enforcement agencies. This paper explores the use of Machine Learning (ML) methods in comparison with standard
statistical regression techniques for developing automated identification methods. In this work, the ML task is broken into
two sub-tasks, data reduction and prediction. In well-conditioned data, the number of samples should be much larger than
the number of attributes per sample, to limit the degrees of freedom in predictive models. In this spectroscopy data, the
opposite is normally true. Predictive models based on such data have a high number of degrees of freedom, which increases
the risk of models over-fitting to the sample data and having poor predictive power. In the work described here, an approach
to data reduction based on Genetic Algorithms is described. For the prediction sub-task, the objective is to estimate the
concentration of a component in a mixture, based on its Raman spectrum and the known concentrations of previously seen
mixtures. Here, Neural Networks and k-Nearest Neighbours are used for prediction. Preliminary results are presented for the
problem of estimating the concentration of cocaine in solid mixtures, and compared with previously published results in
which statistical analysis of the same dataset was performed. Finally, this paper demonstrates how more accurate results
may be achieved by using an ensemble of prediction techniques.
Keywords: Forensic science; Narcotics; Regression; Raman; Spectroscopy; Machine Learning; Ensemble; Genetic
Algorithm; Neural Network.
1. INTRODUCTION
Raman spectroscopy is being used in forensic science research for the identification and analysis of narcotics, explosives,
polymers, and other materials.
1 Raman spectra are unique and are based on the vibrational motions of molecules, which
provides a chemical fingerprint suitable for identification and discrimination of a wide range of materials.
2 Furthermore, the
development of fiber optic Raman probes will allow the implementation of portable devices for in-situ examination of
suspect materials including narcotic.
3
Examples of illicit narcotics analysed by Raman spectroscopy in the laboratory
include cocaine,
4 heroin,
5 and amphetamines in both solid and solution.
6, 7 In reality, however, the composition of seized
drug samples can very enormously and it is unlikely that suspect materials will contain only one or two pure diluents. The
vast range of possible diluents and impurities that may be present pose several problems for the use of Raman spectroscopy
for the quantitative and qualitative analysis of illicit drugs. Difficulties include the presence of fluorescent materials, which
obscure Raman peaks, overlap of diluent with narcotic Raman peaks, and variations in signal quality.
To help overcome these problems many investigators a have turned to advanced computational methods to improve the
quantitative and qualitative capability of Raman spectroscopy. For quantitative measurements, the use of chemometrics
(multivariate analysis) has been demonstrated in the prediction of fuel composition,
8 metabolites in urine,
9 and cocaine.
10, 11
In our previous work, we have employed traditional chemometric methods (Partial Least Squares) for the development of
quantitative models for predicting cocaine concentration in solid mixtures.
10, 11 Unfortunately, as the mixtures become more