COMPARISON BETWEEN THE DIRECT TRILINEAR DECOMPOSITION AND THE MULTIVARIATE CURVE RESOLUTION-ALTERNATING LEAST-SQUARES METHODS FOR THE RESOLUTION OF 3-WAY DATA SETS

Direct trilinear decomposition (DTD) and multivariate curve resolution -alternating least squares (MCR-ALS) methods are two of the most repre sentative three-way resolution procedures. The former, non-iterative, is based on the resolution of the generalized eigenvector/eigenvalue p roblem and the latter, iterative, is focused on the optimization of in itial estimates by using data structure and chemical constraints. DTD and MCR-ALS have been tested on a variety of three-way simulated data sets having common sources of variation in real response profiles, suc h as signal shift, broadening or shape distortions caused by noise. Th e effect of these factors on the resolution results has been evaluated through the analysis of several parameters related to the recovery of both qualitative and quantitative information and to the quality of t he overall data description. Conclusions inferred from the simulated e xamples help to clarify the performance of both methods on a real exam ple and to provide some general guidelines to understand better the po tential of each method. (C) 1998 Elsevier Science B.V. All rights rese rved.