1. Class identity assignment for amphetamines using neural networks and GC-FTIR data.
- Author
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Gosav S, Praisler M, Van Bocxlaer J, De Leenheer AP, and Massart DL
- Subjects
- Chromatography, Gas methods, Reproducibility of Results, Sensitivity and Specificity, Spectroscopy, Fourier Transform Infrared methods, Amphetamines analysis, Central Nervous System Stimulants analysis, Neural Networks, Computer
- Abstract
An exploratory analysis was performed in order to evaluate the feasibility of building of neural network (NN) systems automating the identification of amphetamines necessary in the investigation of drugs of abuse for epidemiological, clinical and forensic purposes. A first neural network system was built to distinguish between amphetamines and nonamphetamines. A second, more refined system, aimed to the recognition of amphetamines according to their toxicological activity (stimulant amphetamines, hallucinogenic amphetamines, nonamphetamines). Both systems proved that discrimination between amphetamines and nonamphetamines, as well as between stimulants, hallucinogens and nonamphetamines is possible (83.44% and 85.71% correct classification rate, respectively). The spectroscopic interpretation of the 40 most important input variables (GC-FTIR absorption intensities) shows that the modeling power of an input variable seems to be correlated with the stability and not with the intensity of the spectral interaction. Thus, discarding variables only because they correspond to spectral windows with weak absorptions does not seem be not advisable.
- Published
- 2006
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