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dc.contributor.authorMalavolta, Marta
dc.contributor.authorMartos Núñez, María Vanesa
dc.identifier.citationMalavolta, M... [et al.]. A survey on computational taste predictors. Eur Food Res Technol (2022). []es_ES
dc.description.abstractTaste is a sensory modality crucial for nutrition and survival, since it allows the discrimination between healthy foods and toxic substances thanks to five tastes, i.e., sweet, bitter, umami, salty, and sour, associated with distinct nutritional or physiological needs. Today, taste prediction plays a key role in several fields, e.g., medical, industrial, or pharmaceutical, but the complexity of the taste perception process, its multidisciplinary nature, and the high number of potentially relevant players and features at the basis of the taste sensation make taste prediction a very complex task. In this context, the emerging capabilities of machine learning have provided fruitful insights in this field of research, allowing to consider and integrate a very large number of variables and identifying hidden correlations underlying the perception of a particular taste. This review aims at summarizing the latest advances in taste prediction, analyzing available food-related databases and taste prediction tools developed in recent years.es_ES
dc.description.sponsorshipPolitecnico di Torino within the CRUI-CARE Agreementes_ES
dc.description.sponsorshipEuropean Union's Horizon 2020 research and innovation program 872181es_ES
dc.rightsAtribución 3.0 España*
dc.subjectMachine learninges_ES
dc.subjectTaste predictiones_ES
dc.subjectMolecular descriptorses_ES
dc.subjectSmall compoundses_ES
dc.subjectFood es_ES
dc.titleA survey on computational taste predictorses_ES

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Atribución 3.0 España
Except where otherwise noted, this item's license is described as Atribución 3.0 España