Abstract: Spectroscopic techniques combined with chemometrics are a promising tool for analysis of seized drug powders. In this study, the performance of three spectroscopic techniques [Mid-InfraRed (MIR), Raman and Near-InfraRed (NIR)] was compared. In total, 364 seized powders were analyzed and consisted of 276 cocaine powders (with concentrations ranging from 4 to 99 w%) and 88 powders without cocaine. A classification model (using Support Vector Machines [SVM] discriminant analysis) and a quantification model (using SVM regression) were constructed with each spectral dataset in order to discriminate cocaine powders from other powders and quantify cocaine in powders classified as cocaine positive. The performances of the models were compared with gas chromatography coupled with mass spectrometry (GC-MS) and gas chromatography with flame-ionization detection (GC-FID). Different evaluation criteria were used: number of false negatives (FNs), number of false positives (FPs), accuracy, root mean square error of cross-validation (RMSECV) and determination coefficients (R-2). Ten colored powders were excluded from the classification data set due to fluorescence background observed in Raman spectra. For the classification, the best accuracy (99.7%) was obtained with MIR spectra. With Raman and NIR spectra, the accuracy was 99.5% and 98.9%, respectively. For the quantification, the best results were obtained with NIR spectra. The cocaine content was determined with a RMSECV of 3.79% and a R-2 of 0.97. The performance of MIR and Raman to predict cocaine concentrations was lower than NIR, with RMSECV of 6.76% and 6.79%, respectively and both with a R-2 of 0.90. The three spectroscopic techniques can be applied for both classification and quantification of cocaine, but some differences in performance were detected. The best classification was obtained with MIR spectra. For quantification, however, the RMSECV of MIR and Raman was twice as high in comparison with NIR. Spectroscopic techniques combined with chemometrics can reduce the workload for confirmation analysis (e.g., chromatography based) and therefore save time and resources.

Abstract: Large quantities of illicit drugs are frequently seized by law enforcement. In such cases, a representative number of samples needs to be quickly examined prior to destruction. No procedure has yet been set up which rapidly provides information regarding the homogeneity of the samples, the presence of controlled substances and the degree of purity. This study establishes a protocol for fast analysis of cocaine and its most common cutting agent, levamisole, in large seizures. The protocol is based on a hypergeometric sampling approach combined with FTIR spectrometry and Support Vector Machines (SVM) algorithms as analysis methods. To demonstrate the practical use of this approach, five large cocaine seizures (consisting between 45 and 85 units) were analysed simultaneously with GC-MS, GC-FID and a portable FTIR spectrometer using Attenuated Total Reflectance (ATR) sampling combined with SVM models. According to the hypergeometric sampling plan of the Drugs Working Group ENFSI guidelines, the required number of subsamples ranged between 19 and 23. Considering the identification analyses, the SVM models detected cocaine and levamisole in all subsamples of cases 1 to 5 (100% correct classification), which was confirmed by GC-MS analysis. Considering the quantification analyses, the SVM models were able to estimate the cocaine and levamisole content in each subsample, compared to GC-FID data. The developed strategy is easy, cost effective and provides immediate information about both the presence and concentration of cocaine and levamisole. By using this new strategy, the number of confirmation analyses with laborious and expensive chromatographic techniques could be significantly reduced.