Qualitative detection of illegal drugs (cocaine, heroin and MDMA) in seized street samples based on SFS data and ANN: validation of method

In this paper, the validation procedure of spectral fluorescence signature (SFS) method combined with multilayer perceptron artificial neural networks (MLP‐ANNs) for detection of illegal drugs (cocaine, heroin and 3,4‐methylenedioxy‐N‐methylamphetamine) in street samples is proposed. The qualitative information, based on a binary response (detected/not detected), was directly obtained through the response of an expert system. The performance parameters (limit of detection, selectivity/matrix effects, threshold value and robustness) were evaluated according to the requirements for qualitative method. Copyright © 2012 John Wiley & Sons, Ltd.

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