Wavelength interval selection in multicomponent spectral analysis by moving window partial least-squares regression with applications to mid-infrared and near-infrared spectroscopic data.
A new wavelength interval selection procedure, moving window partial least-squares regression (MWPLSR), is proposed for multicomponent spectral anal. This procedure builds PLS models in a window that moves over the whole spectral region and then locates useful spectral intervals in terms of the least complexity of PLS models reaching a desired error level. Based on a proposed theory demonstrating the necessity of wavelength selection, MWPLSR provides a viable approach to eliminate the extra variability generated by noncompn.-related factors such as the perturbations in exptl. conditions and phys. properties of samples. A salient advantage of MWPLSR is that the calibration model is very stable against the interference from noncompn.-related factors. also, the selection of spectral intervals in terms of the least model complexity enables the redn. of the size of a calibration sample set in calibration modeling. Two strategies are suggested for coupling the MWPLSR procedure with PLS for multicomponent spectral anal.: One is the inclusion of all selected intervals to develop a PLS calibration model, and the other is the combination of the PLS models built sep. in each interval. The combination of multiple PLS models offers a novel potential tool for improving the performance of individual models. The proposed procedures are evaluated using two open-path FTIR data sets and one near-IR data set, each having different noise characteristics. The results reveal that the proposed procedures are very promising for vibrational spectroscopy-based multicomponent analyses and give much better prediction than the full-spectrum PLS modeling.
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