To train the model, a random subset was then extracted, selecting 360 sweet, 360 bitter, 227 umami and 360 other chemicals. Moreover, the umami class was oversampled with additional 133 umami samples to match the size of the other classes using the Adaptive Boosting (AdaBoost) algorithm, which will be described in detail in the Model construction and performance evaluation section. The remaining 1544 sweet, 1577 bitter and 289 other peptides were used for external testing to examine the generalization properties of the trained models. A table summarising the division between the training and the test set is reported in the Supplementary Data (Supplementary Table ).