PLSR, PLS-DA, SVMR and SVM-DA were developed using the PLS toolbox libraries. The hyperparameters' optimization was carried out using a 10 splits Venetian Blinds cross-validation (CV) (i.e., 10-fold CV with systematic interleaving). The optimal number of latent variables (LVs) of the PLS and PLS-DA models was optimized by minimizing the RMSECV (root mean square error of CV) and the CV classification error for regression and discriminant analysis, respectively. Regarding SVM, the radial basis function (RBF) was chosen as the kernel function to effectively capture non-linear relationships within the data. The optimal parameters, (C and γ), were determined using a grid search approach, aiming to minimize crossvalidation (CV) error. The range explored for C and γ values were 10 -3 to 100 and 10 -6 to 10, respectively. RF models were developed using the statistical and machine learning toolbox from Matlab. The number of learning cycles, the learn rate and the maximum number of splits were optimized using bayesian optimization.