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

Mazina Jekaterina | Zendy; Aleksejev Valeri | Zendy; Ivkina Tatjana | Zendy; Kaljurand Mihkel | Zendy; Poryvkina Larissa | Zendy

Premium

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.

This content is not available in your region!

Continue researching here.

Having issues? You can contact us here

Accelerating Research