Mutlivariate calibration with Raman data using fast principal component regression and partial least squares methods

Linear and non-linear calibration methods (principal component regression ( PCR), partial least squares regression (PLS), and neural networks (NN)) wer e applied to a slightly non-Linear Raman data set. Because of the large siz e of this data set, recently introduced linear calibration methods, specifi cally optimised for speed, were also used. These fast methods achieve speed improvement by using the Lanczos decomposition for the singular value deco mposition steps of the calibration procedures, and for some of their varian ts, by optimising the models without cross-validation (CV). Linear methods could deal with the slight non-linearity present in the data by including e xtra components, therefore, performing comparably to NNs. The fast methods performed as well as their classical equivalents in terms of precision in p rediction, but the results were obtained considerably faster. It, however, appeared that CV remains the most appropriate method for model complexity e stimation. (C) 2001 Elsevier Science B.V All rights reserved.