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


Abstract: 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.

Keywords: Principal component analysis, Soft Independent Modeling of Class Analogy, Infrared spectrum, Amphetamines, Drugs of abuse

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