Classifying taste using explainable AI
15 November 2022
IDSIA’s researchers Gabriele Maroni, Dario Piga and Gianvito Grasso have contributed to a research study focused on the development of a virtual machine able to accurately predict sweeteners/bitterants compounds starting from their chemical composition.
The study is entitled “Informed Classification of Sweeteners/Bitterants Compounds via Explainable Machine Learning” and has been published on the journal “Current Research in Food Science” (IF: 6.269). The work represents a crucial starting point in the definition of a virtual tongue able to predict the taste of specific ingredients with the ultimate goal of shedding light on the mechanisms at the basis of the taste perception process.
The research has been conducted in collaboration with the Politecnico di Torino and the Italian company 7HC srl, and lies within the framework of the EU H2020 VIRTUOUS project, funded by the European Union under the Marie Sklodowska-Curie Actions Program.
The study is entitled “Informed Classification of Sweeteners/Bitterants Compounds via Explainable Machine Learning” and has been published on the journal “Current Research in Food Science” (IF: 6.269). The work represents a crucial starting point in the definition of a virtual tongue able to predict the taste of specific ingredients with the ultimate goal of shedding light on the mechanisms at the basis of the taste perception process.
The research has been conducted in collaboration with the Politecnico di Torino and the Italian company 7HC srl, and lies within the framework of the EU H2020 VIRTUOUS project, funded by the European Union under the Marie Sklodowska-Curie Actions Program.
Link to the paper