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The development of simple and cost-effective analytical methods is indispensable for law enforcement officials in the rapid screening for large quantities of seized drugs. Handheld infrared spectroscopy has been used with Bayes discrimination analysis (BDA), k nearest neighbor (KNN), support vector machine (SVM), decision-making tree (DT), and random forest (RF) chemometrics to characterize 3,4-methylenedioxy-N-methylamphetamine (MDMA), ketamine, and benzodiazepine samples. Spectral data were pretreated by normalization, multivariate scatter correction, Savitzky-Golay smoothing algorithm, and automatic baseline correction. Principal component analysis (PCA) was employed to reduce the dimensionality of spectral data, while BDA, KNN, SVM, DT, and RF, as supervised pattern recognition methods, were implemented and their parameters were optimized to identify the samples. The results showed that SVM was optimal among the models, followed by DT, KNN, BDA, and RF. The optimal model, based upon polynomial kernel function, achieved an accuracy of 100% (training set) and 100% (test set) for all samples and was used to further distinguish the additives in MDMA, ketamine, and benzodiazepine samples. In the MDMA samples, 42 samples contained caffeine, 23 contained glucose, and 21 contained starch. In the ketamine samples, 34 contained caffeine, 13 contained glucose, and 12 contained phenacetin. In the benzodiazepine samples, estazolam, diazepam, lorazepam, clonazepam, and nitrazepam were identified. 5 samples were misidentified into other categories. This study demonstrated that handheld infrared spectroscopy with chemometrics is a reliable and desirable approach for the rapid and nondestructive identification of MDMA, ketamine, and benzodiazepine with high potential in practical applications.

analytical letters

Forensic Identification of 3,4-Methylendioxy-N-methylamphetamine (MDMA), Ketamine, and Benzodiazepine by Handheld Infrared Spectroscopy and Chemometrics