Choosing between GC-FTIR and GC-MS spectra for an efficient intelligent identification of illicit amphetamines

Abstract In this paper we are presenting a comparative analysis between several expert systems built for the identification of illicit amphetamines based on their GC–FTIR and GC–MS spectra. The systems were built using Artificial Neural Networks (ANNs), and are dedicated to the recognition of amphetamines. Structure–activity relationships are incorporated into the knowledge base, allowing the systems to identify the amphetamines according to their toxicological activity (stimulant or hallucinogenic). The results show that GC–FTIR data are much more relevant for the efficiency of the expert systems, probably due to the fact that these spectra constitute a “fingerprint” of the molecular structures. We are also presenting a spectroscopic analysis in order to evaluate the relevance of each type of input variable (absorption and abundance) on which the recognition of an unknown sample is based.