Kohonen artificial neural networks as a tool for wavelenght selection in multicomponent spectrofluorimetric PLS modelling: application on phenol, o-cresol, m-cresol and p-cresol mixtures.

Different strategies for wavelength selection for partial least squared (PLS) calibration models have been proposed. In this article, Kohonen artificial neural networks (K-ANN) are used to select optimal sets of wavelengths for PLS calibration of mixtures with stray overlapping. This kind of variable selection appears simple and very effective due to the well known high correlation of spectroscopic data; a measure of the multivariate correlation of the different wavelength subsets is also given. This strategy has been applied to the resolution of mixtures of phenol, o-cresol, m-cresol and p-cresol by spectrofluorimetry. The number of samples to obtain the calibration matrix is also reduced with respect to the number necessary when the full spectrum is used, and the predictive ability of the PLS method is improved. Copyright (C) 1999 Elsevier Science B.V.