Rapid Classification of Simulated Street Drug Mixtures Using Raman Spectroscopy and Principal Component Analysis

Abstract

The ability to accurately and noninvasively analyze illicit drugs is important for criminal investigations and prosecution. Current methods involve significant sample pretreatment and most are destructive. The goal of this work is to develop a method based on Raman spectroscopy to classify simulated street drug mixtures composed of one drug component and up to three cutting agents including those routinely found in confiscated illicit street drug mixtures. Spectra were collected on both a homebuilt instrument using a HeNe laser and on a handheld commercial instrument with a 785 nm light source. Mixtures were prepared with drug concentrations ranging from 10 to 100 percent. Optimal preprocessing for the data set included truncating, Savitzky–Golay smoothing, normalization, differentiating, and mean centering. Using principal component analysis (PCA), it was possible to resolve the spectral differences between benzocaine, lidocaine, isoxsuprine, and norephedrine and correctly classify them 100 percent of the time.