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On transmission spectra, DLSNV and PPSNV perform better than LSNV, but PSNV only has a positive effect when compared to SNV.
Although the sophisticated preprocessing methods LSNV, DLSNV, PNSV, and PPSNV show a lower initial calibration error, the slope of the error is lower than for SNV.
The LSNV optimization process selects the same window size as DLSNV.
Table 2 also lists the relative improvements against the benchmark preprocessing, LSNV, accompanied by corresponding p values from a two-sided t-test, which tests the significance of the mean values being different (the deviation for relative improvements when the same mean value is given due to the fact that the improvements were calculated from exact values rather than rounded values).
PSNV is not reasonable for the antioxidant additive as the performance is poor compared with the benchmark preprocessing method LSNV.
Against the benchmark LSNV, an enhancement of the predictive power of a ridge regression model by up to 16% and 29% could be achieved for the friction modifier and the antioxidant compound, respectively.
Boxplot (1) is without standardization, (2) is with a single SNV transformation on the full spectrum, (3) is with optimized LSNV, (4) is with optimized Dynamic DLSNV, (5) is with optimized PSNV, and (6) is with optimized PPSNV.