Characterization of Heroin and Its Additives by Attenuated Total Reflection (ATR) – Fourier Transform Infrared Spectroscopy (FTIR) and Multivariate Analysis

A method using attenuated total reflectance (ATR) Fourier transform infrared (FTIR) spectroscopy and multivariate analysis is reported to classify heroin hydrochloride and five common additives: caffeine, phenacetin, starch, glucose, and sucrose. Baseline correction, multivariate scatter correction, standard normal variables and Savitzky-Golay algorithm using smoothing with a polynomial order of three and a window size of seven points were adopted to preprocess the spectral data. Several supervised pattern recognition methods including radial basis function neural network (RBFNN), multilayer perceptron neural network (MLPNN), and linear fitting analysis (LFA) were used as algorithms to construct the classifiers. The use of a characteristic spectrum had lower complexity, required reduced modeling time, and was able to achieve the goal of rapid classification. Mixtures of heroin hydrochloride with caffeine, heroin hydrochloride and phenacetin, heroin hydrochloride with sucrose, heroin hydrochloride and starch, and heroin hydrochloride with glucose were distinguished with accuracy values of 100%, 100%, 88.89%, 77.78% and 66.67%, respectively. The fitting ability of the quadratic polynomial function was superior than using a linear model. A linear model was shown to be optimal when the additive was phenacetin, although the quadratic function was more superior when the additive was glucose. The developed method represents a simple, nondestructive, and rapid approach to classify mixtures of heroin hydrochloride with its common additives, caffeine, glucose, phenacetin, starch and sucrose.