The multi-objective evolutionary optimization algorithm employed in our study indicated that the RF method achieved better performance across multiple objectives compared to SVM. Therefore, we developed and evaluated 20 different RF models (see also Supplementary Table ). Among these models, we selected the best model (RF model #2 in Supplementary Table ) considering the balance between the achieved performance and the relatively small number of features (15). A summary of the model performance is reported in Table . During the training phase, we employed 10-fold crossvalidation (CV) on the training set, presenting mean values and standard deviations for the performance metrics. The testing set comprises the 3377 left-out samples not used for training, consisting of 1577 bitter, 1544 sweet and 289 other compounds. The relative ROC curves are represented in Fig. . In Table and Table , a summary of the performance of each taste class is presented for the training set and the testing set, respectively. The relative ROC curves from the validation of the testing set are represented in Fig. .