Handling non-linearities and pre-processing in multivariate calibration of vibrational spectra

Abstract

Background

several automatic pre-processing methods are available for coping with scattering effects in vibrational spectra (near and mid infrared, Raman) when linear multivariate models are applied for calibration. In contrast, almost no methods have been developed for data sets showing a non-linear relationship between instrumental signals and analyte concentrations or target properties.

Results

several data sets are studied, both simulated and experimental, showing different degrees of non-linearity: very low, intermediate, and high. For each of them, three multivariate models have been applied: classical partial least-squares (PLS), sequential pre-processing through orthogonalization (SO-PLS) and Kernel partial least-squares (K-PLS). The success of these models depends on the degree of non-linearity presented by each data set. The results are discussed in terms of the model structures.

Significance

the need of developing new automatic pre-processing techniques for non-linear vibrational data sets is highlighted, in order to reach a similar status as the linear counterparts.