Automated Recognition of Ergogenic Aids Using Soft Independent Modeling of Class Analogy (SIMCA)

M. PRAISLER

Makale detayları

Automated Recognition of Ergogenic Aids Using Soft Independent Modeling of Class Analogy (SIMCA)

Yazarlar:

M. PRAISLER

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Chemistry 2002 Vol.26 Issue 1, pp.45-58

Belge Tipi:

Makale

Yayın Tarihi:

12/2001

Belge Dili:

Türkçe

Document ID (Org No.):

2000729999

Sıklık:

İki Aylık

eISSN:

1303-6130

pISSN:

1300-0527

Özet:

 

The introduction of effective drug testing procedures in doping control   reduced, but did not eliminate, their abuse by athletes. The most important   analytical challenge is the recognition of the new analog compounds. In   attempts to circumvent existing controlled substance laws, slightly modified   chemical structures (by adding or changing substituents at various positions   on the banned molecules) are used. As a result, no substance belonging to a   prohibited class may be used nowadays, even if it has not been specifically   listed. We  present a chemometric procedure acting as an automated   GC-FTIR screening test detecting the molecular structural similarity of   unknown compounds with the main classes of ergogenic aids. The knowledge   base defining the reference GC-FTIR spectral patterns has been built   according to criteria encompassing toxicological, pharmacological and   neurochemical aspects. The class identity of a compound is diagnosed within   seconds, using Soft Independent Modeling of Class Analogy (SIMCA). The   predictive value of the system was assessed at a testing accuracy of 95%.   Compounds giving cross-reactions with traditional screening techniques   produce a negative result. The specificity and the selectivity of the   screening test, evaluated by testing 160 toxicologically relevant compounds,   are discussed, emphasizing the chemical and physical factors affecting these   parameters. The specificity of the system recommends the procedure as a   highly specific, selective, fast, and user-friendly screening test, which   screens for ergogenic aids found in powders, tablets or solutions with a   prediction accuracy adequate for investigations in analytical toxicology and   doping control.

 

The introduction of effective drug testing procedures in doping control   reduced, but did not eliminate, their abuse by athletes. The most important   analytical challenge is the recognition of the new analog compounds. In   attempts to circumvent existing controlled substance laws, slightly modified   chemical structures (by adding or changing substituents at various positions   on the banned molecules) are used. As a result, no substance belonging to a   prohibited class may be used nowadays, even if it has not been specifically   listed. We  present a chemometric procedure acting as an automated   GC-FTIR screening test detecting the molecular structural similarity of   unknown compounds with the main classes of ergogenic aids. The knowledge   base defining the reference GC-FTIR spectral patterns has been built   according to criteria encompassing toxicological, pharmacological and   neurochemical aspects. The class identity of a compound is diagnosed within   seconds, using Soft Independent Modeling of Class Analogy (SIMCA). The   predictive value of the system was assessed at a testing accuracy of 95%.   Compounds giving cross-reactions with traditional screening techniques   produce a negative result. The specificity and the selectivity of the   screening test, evaluated by testing 160 toxicologically relevant compounds,   are discussed, emphasizing the chemical and physical factors affecting these   parameters. The specificity of the system recommends the procedure as a   highly specific, selective, fast, and user-friendly screening test, which   screens for ergogenic aids found in powders, tablets or solutions with a   prediction accuracy adequate for investigations in analytical toxicology and   doping control.

Makale detayları

Automated Recognition of Ergogenic Aids Using Soft Independent Modeling of Class Analogy (SIMCA)

Yazarlar: M. PRAISLER

Yayıncı: TUBITAK

Dergi: Turkish Journal of Chemistry 2002 Vol.26 Issue 1, pp.45-58

Belge Tipi: Makale

Yayın Tarihi: 12/2001

Belge Dili: Türkçe

Document ID (Org No.): 2000729999

Sıklık: İki Aylık

eISSN: 1303-6130

pISSN: 1300-0527

Sayı

Yazarlar:

M. PRAISLER

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Chemistry 2002 Vol.26 Issue 1, pp.45-58

Belge Tipi:

Makale

Yayın Tarihi:

12/2001

Belge Dili:

Türkçe

Document ID (Org No.):

2000729999

Sıklık:

İki Aylık

eISSN:

1303-6130

pISSN:

1300-0527