Classification of heroin, methamphetamine, ketamine and their additives by attenuated total reflection-Fourier transform infrared spectroscopy and chemometrics

Abstract

Drug crime is a prominent issue of concern from pole to pole. In order to seek higher profits, drug gangs often add diluents and adulterants to the drugs to disperse drug products Analysis of these additives would be greatly conducive to determine the origin of drug products for law enforcement departments. A method using attenuated total reflectance-Fourier transform infrared spectroscopy and chemometrics methods to classify the heroin hydrochloride, methamphetamine hydrochloride, ketamine hydrochloride and their five additives (caffeine, phenacetin, starch, glucose, and sucrose), was developed. The Baseline correction, multivariate scatter correction, standard normal variate and Savitzky-Golay algorithm were adopted to pre-process the spectral data. Several supervised pattern recognition methods including decision tree, Bayes discriminant analysis, and support vector machine were considered as algorithms of constructing classifiers. The results reveal that, repetitive and interfering data in original spectrum data could be eliminated by principal component analysis and factor analysis. F-measure, as a comprehensive evaluation index of precision rate and recall rate, was more objective than precision rate and recall rate to reflect the ability of model to distinguish samples. It should be used as one of the indicators to evaluate the model. The CHAID classification tree could be identified as priorities in the decision tree model, while the linear kernel could be considered as the optimal kernel in the support vector machine model. The classification ability of three hydrochloride mixtures based on Bayes discriminant analysis was better than that of another models. Bayes discriminant analysis model was the more useful and practical method for classifying the target drugs of abuse than that of decision trees and support vector machine. The designed approach represents a potentially simple, non-destructive, and rapid method of classifying hydrochloride mixtures.

Keywords: Additives; Attenuated total reflectance-Fourier transformed infrared spectroscopy; Bayes discriminant analysis; Decision tree; Drug hydrochloride; Extraction of feature variables; Support vector machine.