This paper presents electrochemical strategies for the fast screening of cocaine and most common cutting agents found in seized drug samples. First, a study on the performance of Scott color tests on cocaine and a wide range of cutting agents is described. The cutting agents causing false positive or false negative results when in mixture with cocaine are identified. To overcome the lack of specificity of color tests, we further propose a fast screening strategy by means of square wave voltammetry on disposable graphite screen printed electrodes, which reveals the unique fingerprint of cocaine and cutting agents. By employing a forward and backward scan and by a dual pH strategy, we enrich the electrochemical fingerprint and enable the simultaneous detection of cocaine and cutting agents. The effectiveness of the developed strategies was tested for the detection of cocaine in seized cocaine samples and compared with the color tests. Moreover, we prove the usefulness of square wave voltammetry for predicting possible interfering agents in color tests, based on the reduction peak of cobalt thiocyanate. The developed electrochemical strategies allow for a quick screening of seized cocaine samples resulting in a selective identification of drugs and cutting agents.

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.

Electrochemical strategies to selectively detect heroin in street samples without the use of complicated electrode modifications were developed for the first time. For this purpose, heroin, mixing agents (adulterants, cutting agent and impurities) and their binary mixtures were subjected to square wave voltammetry measurements at bare graphite electrodes at pH 7.0 and pH 12.0, in order to elucidate the unique electrochemical fingerprint of heroin and mixing agents as well as possible interferences or reciprocal influences. Adjusting the pH from pH 7.0 to pH 12.0 allowed a more accurate detection of heroin in the presence of most common mixing agents. Furthermore, the benefit of introducing a preconditioning step prior to running square wave voltammetry on the electrochemical fingerprint enrichment was explored. Mixtures of heroin with other drugs (cocaine, 3,4-methylenedioxymethamphetamine and morphine) were also tested to explore the possibility of their discrimination and simultaneous detection. The feasibility of the proposed electrochemical strategies was tested on realistic heroin street samples from forensic cases, showing promising results for fast, on-site detection tools of drugs of abuse.

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