Abstract

The utility of artificial neural networks (ANN) as a tool for confirmation of the identity of infrared (IR) spectra has been investigated. The main goal of the study is the identification of spectra with relatively high noise levels, obtained from gas chromatography combined with IR spectrometric detection. Networks were trained for GC/IR spectra of Clenbuterol, Fluoranthene and Perylene as representatives of compounds for which identification in real world samples is demanded occasionally. Results have been compared with classification by peak matching and library search methods. Peak matching appears to be the most discriminative method to distinguish between closely resembling spectra, but only in case of high signal-to-noise ratio and resolution. Similar conclusions are drawn for library search identification. ANN-models are less sensitive to spectral noise and hence most suited to be used for confirmation and identification of spectra obtained intrace analysis.