Vis-NIR spectrometric determination of Brix and sucrose in sugar production samples using kernel partial least squares with interval selection based on the successive projections algorithm

This paper proposes a new variable selection method for nonlinear multivariate calibration, combining the Successive Projections Algorithm for interval selection (iSPA) with the Kernel Partial Least Squares (Kernel-PLS) modelling technique. The proposed iSPA-Kernel-PLS algorithm is employed in a case study involving a Vis-NIR spectrometric dataset with complex nonlinear features. The analytical problem consists of determining Brix and sucrose content in samples from a sugar production system, on the basis of transflectance spectra. As compared to full-spectrum Kernel-PLS, the iSPA-Kernel-PLS models involve a smaller number of variables and display statistically significant superiority in terms of accuracy and/or bias in the predictions. [Display omitted] •Vis-NIR spectrometric determination of Brix and sucrose content in samples from a sugar production system.•New interval selection method for nonlinear multivariate calibration, combining SPA with Kernel-PLS.•Models using fewer spectral variables, with better parsimony and simpler interpretation.•Improvements on full-spectrum Kernel-PLS regarding accuracy and/or bias in the predictions.