Literature DB >> 18968103

Pattern recognition techniques screening for drugs of abuse with gas chromatography-Fourier transform infrared spectroscopy.

M Praisler1, I Dirinck, J Van Bocxlaer, A De Leenheer, D L Massart.   

Abstract

As many drugs of abuse are relatively volatile substances, gas chromatography-mass spectrometry (GC-MS), and more recently gas chromatography-Fourier transform infrared spectroscopy (GC-FTIR) became the most powerful techniques applied for their identification. We are presenting a combination of pattern recognition techniques discriminating illicit amphetamines according to the substitution pattern associated with the psychotropic activity (stimulants and hallucinogens) for which they are abused, and with the corresponding level of health hazard. As we determined, GC-FTIR provides the best selectivity in identifying the structural features associated with the full constellation of pharmacological effects of amphetamines. The toxicological questions to be answered and the spectroscopic features enabling the screening based on soft independent modeling of class analogy (SIMCA) are discussed. The accuracy, sensitivity and selectivity of the system recommend its use for automating the investigations of illicit drugs for epidemiological, clinical, administrative and forensic purposes. As opposed to the traditional tests screening for drugs of abuse, the system may also be applied as a broad-spectrum screening test. The extent to which the output of a query for amphetamines may be used for assessing the class identity of a negative (i.e. other hallucinogens or stimulants, sympathomimetic amines, narcotics and precursors) was determined by a systematic principal component analysis (PCA). The basic information is summarized in tables according to the category or class of compounds found suitable for screening.

Entities:  

Year:  2000        PMID: 18968103     DOI: 10.1016/s0039-9140(00)00460-4

Source DB:  PubMed          Journal:  Talanta        ISSN: 0039-9140            Impact factor:   6.057


  1 in total

1.  Principal component analysis coupled with artificial neural networks--a combined technique classifying small molecular structures using a concatenated spectral database.

Authors:  Steluţa Gosav; Mirela Praisler; Mihail Lucian Birsa
Journal:  Int J Mol Sci       Date:  2011-10-11       Impact factor: 5.923

  1 in total

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