A survey on computational taste predictors
Metadatos
Mostrar el registro completo del ítemEditorial
Springer
Materia
Machine learning Taste prediction Molecular descriptors Small compounds Tastants Food
Fecha
2022-05-26Referencia bibliográfica
Malavolta, M... [et al.]. A survey on computational taste predictors. Eur Food Res Technol (2022). [https://doi.org/10.1007/s00217-022-04044-5]
Patrocinador
Politecnico di Torino within the CRUI-CARE Agreement; European Union's Horizon 2020 research and innovation program 872181Resumen
Taste 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.