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Predicting 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.
Afiliação
  • Androutsos L; InSyBio PC, Patras, 265 04, Greece.
  • Pallante L; PolitoBIOMedLab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Torino, 10129, Italy.
  • Bompotas A; Industrial Systems Institute, Athena Research Center, 265 04, Patras, Greece.
  • Stojceski F; Department of Innovative Technologies, Dalle Molle Institute for Artificial Intelligence, Lugano-Viganello, 6962, Switzerland.
  • Grasso G; Department of Innovative Technologies, Dalle Molle Institute for Artificial Intelligence, Lugano-Viganello, 6962, Switzerland.
  • Piga D; Department of Innovative Technologies, Dalle Molle Institute for Artificial Intelligence, Lugano-Viganello, 6962, Switzerland.
  • Di Benedetto G; Enginlife Engineering Solutions, Turin, Italy.
  • Alexakos C; Industrial Systems Institute, Athena Research Center, 265 04, Patras, Greece.
  • Kalogeras A; Industrial Systems Institute, Athena Research Center, 265 04, Patras, Greece.
  • Theofilatos K; InSyBio PC, Patras, 265 04, Greece. k.theofilatos@insybio.com.
  • Deriu MA; PolitoBIOMedLab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Torino, 10129, Italy.
  • Mavroudi S; InSyBio PC, Patras, 265 04, Greece.
NPJ Sci Food ; 8(1): 47, 2024 Jul 25.
Article em En | MEDLINE | ID: mdl-39054312
ABSTRACT
Taste 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.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article