Partial least squares regression as a multivariate tool for the interpretation of infrared spectra

The potentials of partial least squares regression (PLS) as a tool for the automated interpretation of infrared spectra have been investigated. Class probabilities are obtained by applying a sigmoid function to the PLS predictions. Results are compared with those obtained from artificial neural networks (ANN) applied to full spectra and to the most relevant principal component scores. The predictions obtained from all approaches appear to be comparable, but the time required for training is considerably shorter for the extended PLS approach.