Rapid classification and quantification of cocaine in seized powders with ATR‐FTIR and chemometrics

Abstract

Traditionally, fast screening for the presence of cocaine in unknown powders is performed by means of colour tests. The major drawbacks of these tests are subjective colour evaluation depending on the operator ('50 shades of blue') and a lack of selectivity. An alternative fast screening technique is Fourier Transform InfraRed (FTIR) spectrometry. This technique provides spectra that are difficult to interpret without specialized expertise and shows a lack of sensitivity for the detection of cocaine in mixtures. To overcome these limitations, a portable FTIR spectrometer using Attenuated Total Reflectance (ATR) sampling was combined with a multivariate technique, called Support Vector Machines (SVM). Representative street drug powders (n = 482), seized during the period January 2013 to July 2015, and reference powders (n = 33) were used to build and validate a classification model (n = 515) and a quantification model (n = 378). Both models were compared with the conventional chromatographic techniques. The SVM classification model showed a high sensitivity, specificity, and efficiency (99%). The SVM quantification model determined cocaine content with a root mean squared error of prediction (RMSEP) of 6% calculated over a wide working range from 4 to 99 w%. In conclusion, the developed models resulted in a clear output (cocaine detected or cocaine not detected) and a reliable estimation of the cocaine content in a wide variety of mixtures. The ATR-FTIR technique combined with SVM is a straightforward, user-friendly, and fast approach for routine classification and quantification of cocaine in seized powders. Copyright © 2016 John Wiley & Sons, Ltd.

Applications of electrochemical sensors: Forensic drug analysis

“…33 This is slightly higher than that obtained for cocaine street samples in Beligum, where typically cocaine purity was found to be above 30%. 34 These values are significantly higher than does reported for ATS street samples, where values ranged from 1.4 to 6% for traditional ATS samples. 35 Within these samples are also a wide range of impurities and street samples also containing from 3 to 10 cutting agents including caffeine, paracetamol, other illicit substances.…”

Practical tool for sampling and fast analysis of large cocaine seizures

“…In order to determine the model parameters, including the number of support vectors (SVs), double cross‐validations (with 5 random subsets and 1 iteration) were performed . The parameters of the SVM‐DA calibration models were the following: for cocaine: SVs = 16 and cost = 0.32 and for levamisole: SVs = 15 and cost = 0.32.…”

“…The root mean squared error of prediction (RMSEP) and correlation coefficients (R 2 ) were used to evaluate the agreement between SVMR and GC‐FID results …”

“…7 Accredited gas chromatography-mass spectrometry (GC-MS) and gas chromatography-flame ionisation detector (GC-FID) analysis (Agilent Technologies, Santa Clara, CA, USA) were used as reference methods for the identification and quantification of the powders. 7…”

“…For the cocaine classification model, the calibration dataset included 515 spectra of 482 authentic street samples (378 with cocaine and 104 without cocaine) and 33 reference materials. 7 For the cocaine quantification model, the calibration set consisted only of the 378 cocaine hydrochloride-containing samples seized during the period from January 2013 to July 2015 (4 to 99 w%). 7 The classification dataset for levamisole consisted of 385 spectra Cocaine and levamisole were identified in all subsamples of Cases 1 to 5 with SVM-DA, which was in agreement with the GC-MS results.…”