A multivariate calibration problem in analytical chemistry solved by partial least-squares models in latent variables

Abstract

The use of partial least squares in latent variables (PLS) for multivariate calibration problems is described. The application is the simultaneous determination of ligninsulfonate, humic acid and an optical whitener, from their severely overlapping fluorescence spectra. The predictive performance of the resulting calibration model is tested with a separate set of samples. The PLS method also identifies samples which do not fit the calibration model. The PLS method is compared with principal components analysis combined with multiple regression.