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The document proposes a multi-class machine-learning predictor to distinguish bitter, sweet, and umami from other taste sensations. This framework supports understanding chemical attributes of fundamental tastes and enables integration into multi-sensory flavour characterization, aiding rational food design and diet engineering for complementary therapeutic strategies.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nPredicting multiple taste sensations with a multiobjective machine learning method  \nOriginal  \nPredicting multiple taste sensations with a multiobjective machine learning method / Androutsos, Lampros; Pallante, Lorenzo; Bompotas, Agorakis; Stojceski, Filip; Grasso, Gianvito; Piga, Dario; Di Benedetto, Giacomo; Alexakos, Christos; Kalogeras, Athanasios; Theofilatos, Konstantinos; Deriu, Marco A. ; Mavroudi, Seferina. -In: NPJ SCIENCE OF FOOD. -ISSN 2396-8370. -8:1(2024) . [10 . 1038/s41538-024-00287-6]  \nAvailability:  \nThis version is available at: 11583/2991267 since: 2024-07-29T10:37:56Z  \nPublisher: Nature  \nPublished  \nDOI:10.1038/s41538-024-00287-6  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \n(Article begins on next page)  \n05 October 2025  \nnpj | science of food Article  \nPublished in partnership with Beijing Technology and Business University  \n[https://doi.org/10.1038/s41538-024-00287-6](https://doi.org/10.1038/s41538-024-00287-6)  \nPredicting multipletaste sensations with amultiobjective machine learning method  \n Check for updates  \nLampros Androutsos 1,7, Lorenzo Pallante 2,7, Agorakis Bompotas 3, Filip Stojceski4,  \nGianvito Grasso4, Dario Piga4, Giacomo Di Benedetto5, Christos Alexakos 3, Athanasios Kalogeras3, Konstantinos Theofilatos 1 , Marco A. Deriu2 & Seferina Mavroudi 1,6  \nTaste perception plays a pivotal role in guiding nutrient intake and aiding in the avoidance of potentially harmful substances through five basic tastes-sweet, bitter, umami, salty, and sour. Taste perception originates from molecular interactions in the oral cavity between taste receptors and chemical tastants. Hence, the recognition of taste receptors and the subsequent perception of taste heavily rely on the physicochemical properties of food ingredients. In recent years, several advances have been made towards the development of machine learning-based algorithms to classify chemical compounds’ tastes using their molecular structures. Despite the great efforts, there remains significant room for improvement in developing multi-class models to predict the entire spectrum of basic tastes. Here, we present a multi-class predictor aimed at distinguishing bitter, sweet, and umami, from other taste sensations. The development of a multi-class taste predictor paves the way for a comprehensive understanding of the chemical attributes associated with each fundamental taste. It also opens the potential for integration into the evolving realm of multi-sensory perception, which encompasses visual, tactile, and olfactory sensations to holistically characterize flavour perception. This concept holds promise for introducing innovative methodologies in the rational design of foods, including pre-determining specific tastes and engineering complementary diets to augment traditional pharmacological treatments.  \nTaste and smell play a pivotal role in the chemosensory perception of food since they are fundamental determinants for the food selection and intake process1. Biochemical compounds derived from food ingestion trigger the taste perception process through the binding with specific proteins known as taste receptors, located on the tongue’s taste buds and dedicated to the recognition of the five basic tastes: sweet, bitter, sour, salty, and umami2,3. Sweet taste is commonly associated with energy-rich food, to help identify sources of sugars and carbohydrates4. Conversely, bitter taste is normally recognized asan unpleasantflavour and acts asa warning against potentially dangerous compounds5. Sour taste helps to detect spoiled food and identify the presence of biologically relevant vitamins6. Salty taste is crucial to monitor the uptake of essential electrolytes, which play a central role in maintaining body osmosis7. Finally, umami taste relates to the protein content in fo","cbCaisgekYF1s4DR","https://ap.wps.com/l/cbCaisgekYF1s4DR","pdf",1785164,1,11,"English","en",105,"# Abstract and Background\n## Taste perception and chemosensory basis\n## Machine learning approaches and research gap\n## Proposed multi-class predictor and applications\n# Model Development and Evaluation\n## Cross-validation performance summary\n## Test set evaluation","[{\"question\":\"What is the main goal of the multiobjective machine learning approach described in the document?\",\"answer\":\"To build a multi-class predictor that distinguishes bitter, sweet, and umami tastes from other taste sensations using machine learning.\"},{\"question\":\"Why does predicting taste sensations matter for nutrition and safety?\",\"answer\":\"Taste perception guides nutrient intake and helps avoid potentially harmful substances by relating food chemical properties to specific taste receptors and basic tastes.\"},{\"question\":\"How can the proposed predictor support future food design or treatments?\",\"answer\":\"It can enable rational design of foods by pre-determining specific tastes and engineering complementary diets, supporting holistic multi-sensory flavour characterization and potentially aiding traditional therapeutic strategies.\"}]","Predicting multiple taste sensations with a multiobjective machine learning method | 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