Classification of a target analyte in solid mixtures using principal component analysis, support vector machines, and Raman spectroscopy

The quantitative analysis of illicit materials using Raman spectroscopy is of widespread interest for law enforcement and healthcare applications. One of the difficulties faced when analysing illicit mixtures is the fact that the narcotic can be mixed with many different cutting agents. This obviously complicates the development of quantitative analytical methods. In this work we demonstrate some preliminary efforts to try and account for the wide variety of potential cutting agents, by discrimination between the target substance and a wide range of excipients. Near-infrared Raman spectroscopy (785 nm excitation) was employed to analyse 217 samples, a number of them consisting of a target analyte (acetaminophen) mixed with excipients of different concentrations by weight. The excipients used were sugars (maltose, glucose, lactose, sorbitol), inorganic materials (talcum powder, sodium bicarbonate, magnesium sulphate), and food products (caffeine, flour). The spectral data collected was subjected to a number of pre-treatment statistical methods including first derivative and normalisation transformations, to make the data more suitable for analysis. Various methods were then used to discriminate the target analytes, these included Principal Component Analysis (PCA), Principal Component Regression (PCR) and Support Vector Machines.

[1]  William W. Cohen.  Fast Effective Rule Induction , 1995, ICML.

[2]  Alan G. Ryder,et al.  Classification of narcotics in solid mixtures using principal component analysis and Raman spectroscopy. , 2002, Journal of forensic sciences.

[3]  S D Harvey,et al.  Blind field test evaluation of Raman spectroscopy as a forensic tool. , 2002, Forensic science international.

[4]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[5]  Pat Langley,et al.  An Analysis of Bayesian Classifiers , 1992, AAAI.

[6]  R. McCreery,et al.  Simplified Calibration of Instrument Response Function for Raman Spectrometers Based on Luminescent Intensity Standards , 1997 .

[7]  J. R. Quinlan.  Learning Logical Definitions from Relations , 1990 .

[8]  Dietrich Wienke,et al.  On the use of recent developments in vibrational spectroscopic instrumentation in an industrial environment: quicker, smaller and more robust , 2000 .

[9]  I. R. Lewis,et al.  Handbook of Raman Spectroscopy: From the Research Laboratory to the Process Line , 2001 .

[10]  S. Bell,et al.  Tracking the distribution of "ecstasy" tablets by Raman composition profiling: a large scale feasibility study. , 2003, The Analyst.

[11]  Gerard M. O'Connor,et al.  Identifications and quantitative measurements of narcotics in solid mixtures using near-IR Raman spectroscopy and multivariate analysis , 1999 .

[12]  Vesa-Pekka Lehto,et al.  Quantitative analysis of amorphous content of lactose using CCD-Raman spectroscopy. , 2005, Journal of pharmaceutical and biomedical analysis.

[13]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[14]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .

[15]  R. McCreery,et al.  Calibration of Raman Spectrometer Instrument Response Function with Luminescence Standards: An Update , 1998 .

[16]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[17]  Yoshua Bengio,et al.  Inference for the Generalization Error , 1999, Machine Learning.

[18]  Alan G. Ryder,et al.  Quantitative analysis of cocaine in solid mixtures using Raman spectroscopy and chemometric methods , 2000 .

[19]  S. Bell,et al.  Rapid analysis of ecstasy and related phenethylamines in seized tablets by Raman spectroscopy. , 2000, The Analyst.

[20]  David N. Batchelder,et al.  Use of a Fiber Optic Probe for the Detection and Identification of Explosive Materials by Raman Spectroscopy , 1995 .