Direct orthogonalization

A multivariate method called direct orthogonalization is proposed for removing factors that describe irrelevant phenomena from data in calibration situations. The method is suggested for improving regression of data sets with systematic, but irrelevant, variations. The method is applied to FT-IR spectral data measured on dry pectin powder samples with the purpose of predicting the degree of esterification. Direct orthogonalization is compared with piecewise multiplicative scatter correction (PMSC) schemes and second order derivatives on the predictive performance of principal component regression (PCR) and partial least squares regression (PLSR) models. When applying direct orthogonalization to the FT-IR spectral data under investigation, the number of significant PLSR and PCR components was lowered significantly while facilitating a qualitative discussion of the scatter phenomena, and at the same time providing a means to identify outliers prior to prediction. In terms of root mean square error of prediction (RMSEP), the proposed method resulted in error measures at the same level as the applied PMSC schemes. Application of second order derivatives to the same data resulted in significantly poorer models.