As in Section 2.1, for supervised multivariate analyses (OPLS-DA in this case), the calculated models were validated, and the robustness and reliability of the models assessed. In addition to the R2 and Q2 metrics, the CV-ANOVA was used to assess the reliability of the obtained models  and the response permutation test (with n = 50) was used to validate the predictive capability of the computed OPLS-DA models . Furthermore, in both Section 2.1 and Section 2.2, predictive testing was also employed to assess the best pre-processing and pre-treatment workflow (Figures S6 and S7). The results tabulated in Table 3 demonstrate that the scaling and transformation methods affected significantly not only the explained variation R2 (both predictive and orthogonal) but also the classification accuracy, reliability, predictive capability of the model and, subsequently, extracted variables (Figure 5). The supervised learning models computed following for instance UV-scaling and/or log-transformation (particularly in this case), would not be chemometrically/statistically trusted as the classification of these models could be by chance, as indicated by the permutation validation tests (lower R2 values compared to the permutated models, Table 3).