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Qualitative detection of illegal drugs (cocaine, heroin and MDMA) in seized street samples based on SFS data and ANN: validation of method
Author(s) -
Mazina Jekaterina,
Aleksejev Valeri,
Ivkina Tatjana,
Kaljurand Mihkel,
Poryvkina Larissa
Publication year - 2012
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2462
Subject(s) - heroin , mdma , detection limit , methylenedioxy , artificial neural network , threshold limit value , artificial intelligence , pattern recognition (psychology) , computer science , chromatography , chemistry , pharmacology , drug , medicine , organic chemistry , alkyl , halogen
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.