Changeable moving window-standard normal variable transformation for visible-NIR spectroscopic analyses

Based on the assumption of point-by-point local linearity, the changeable moving window-standard normal variable (CMW-SNV) was proposed as a reasonable improvement of the classical SNV. The three examples of quantitative and qualitative visible-near-infrared (Vis-NIR) analysis, quantifications of soil organic matter and corn meal moisture, and discriminant of rice seeds identification, were used to validate the effects of the CMW-SNV, SNV and equal segmentation SNV (ES-SNV) methods. The ES-SNV is another improvement of the SNV, but its algorithm would cause artificial discontinuities of the corrected spectrum. The SNV, ES-SNV and CMW-SNV corrected spectra were used to establish partial least squares (PLS) or partial least squares-discriminant analysis (PLS-DA) models respectively. For soil and corn meal datasets in modeling, the CMW-SNV-PLS models were both significantly better than the global SNV-PLS models; the root mean square errors of prediction in modeling (SEPM) values had the relative decrease of 26.4% and 6.6% respectively. For rice seeds dataset in modeling, the CMW-SNV-PLS-DA model was significantly better than the global SNV-PLS-DA model; the total recognition-accuracy rates in modeling (RARM) value increased by 2.1%. For all three datasets, the CMW-SNV models were better than (or close to) ones of the ES-SNV models. The equidistant combination (EC) and wavelength step-by-step phase-out (WSP) methods were used to perform wavelength selection on the CMW-SNV corrected spectra, determining the optimal EC-WSP-PLS or EC-WSP-PLS-DA models. In independent validation of three datasets, the high precision and high recognition accuracy rates validation results were all obtained. The CMW-SNV was a localized natural improvement of the classic global SNV method, and its correction maintained continuity of the spectra. The number of wavelengths m of the correction window represented the scale of localized SNV, and the algorithm platform of CMW-SNV also included the optimization of parameter m, making the localized CMW-SNV method more reasonable.