Recognition of visual characteristics of infrared spectra by artificial neural networks and partial least squares regression

Abstract

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The usefulness of artificial neural networks (ANN) and partial least squares regression (PLS) for computerized interpretation of infrared (IR) spectra has been studied. Experiments have been carried out to establish the capabilities of these methods to recognize characteristic band shapes and patterns as used for the interpretation by experts. Spectra have been classified by (i) the complete spectral profile (ii) the band pattern in a limited preselected region and (iii) individual band shapes. The results are compared with classifications using computer generated frequency/intensity-structure correlations and as performed by experienced spectroscopists. Classification by skilled interpretators is found to be superior in all cases but a significant improvement of the results from ANN and PLS is established compared with predictions obtained from frequency/intensity-structure correlations. Differences in scores between ANN and PLS were small when full spectra or limited spectral regions are considered. Networks scored better in recognizing individual bands. Both the absorption frequency and the band width play an important role in the recognition process.