Thanks to recent scientific progress, these models were outclassed by tree-based models, such as Random Forest (RF) or AdaBoost (AB), and Neural Network (NN) . Within this framework, the latter algorithms also support multi-class classification and work well in the non-linear classification domain, if the selected database is large enough. Multi-class and multi-labelling techniques have been employed in several applications for the food and agricultural industries, but there are still limited applications for classifying compounds by taste and predicting the relative taste intensity . Recently, a multi-class classification method based on learning vector quantization NN to classify tea samples of five commercial brands has been proposed This lack of tools for predicting multiple tastes represents a significant limitation in the field of food science and technology, specifically in the formulation and optimization of food products. In this study, we address these gaps by developing a machine learning-based tool that predicts not one, but four different tastes, and by focusing on the underlying physicochemical properties that contribute to these tastes. In the present work, we aimed at developing a multi-class taste predictor, named VirtuousMultiTaste, able to distinguish between bitter, sweet, and umami from other taste sensations.