Maximum Likelihood Principal Component Analysis as initial projection step in Multivariate Curve Resolution analysis of noisy data

A comparison of the results obtained using three algorithms, Multivariate Curve Resolution Alternating Least Squares (MCR-ALS), Multivariate Curve Resolution Weighted Alternating Least Squares (MCR-WALS) and Maximum Likelihood Principal Component Analysis Multivariate Curve Resolution Alternating Least Squares (MLPCA-MCR-ALS), is presented. The three approaches are applied to the analysis of a simulated environmental data set with error structures of different types and sizes. Special attention is paid to the case of highly heteroscedastic correlated noise. In all cases, the results show that the solutions provided by MLPCA-MCR-ALS are practically identical to those obtained by MCR-WALS. (C) 2012 Elsevier B.V. All rights reserved.