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Predicting multiple taste sensations with a multiobjective machine learning method

Food Science and Technology

Predicting multiple taste sensations with a multiobjective machine learning method

L. Androutsos, L. Pallante, et al.

Discover VirtuousMultiTaste, a groundbreaking multi-class taste predictor developed by leading researchers, designed to differentiate between bitter, sweet, and umami flavors using state-of-the-art machine learning techniques. This innovative tool not only analyzes food ingredients' physicochemical properties but also aids in food design and personalized nutrition. Dive into the future of taste analysis!

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Playback language: English
Abstract
This paper presents a multi-class taste predictor, VirtuousMultiTaste, designed to distinguish bitter, sweet, and umami tastes from other sensations. Utilizing a hybrid approach of heuristic optimization and nonlinear machine learning, the model leverages physicochemical properties of food ingredients to predict taste. The web-based tool (https://virtuous.isi.gr/#/virtuous-multitaste) offers a user-friendly interface for analyzing chemical compounds and understanding their taste profiles, with potential applications in food design and personalized nutrition.
Publisher
npj Science of Food
Published On
Jul 25, 2024
Authors
Lampros Androutsos, Lorenzo Pallante, Agorakis Bompotas, Filip Stojceski, Gianvito Grasso, Dario Piga, Giacomo Di Benedetto, Christos Alexakos, Athanasios Kalogeras, Konstantinos Theofilatos, Marco A. Deriu, Seferina Mavroudi
Tags
taste prediction
machine learning
food science
chemistry
nutrition
bitter
sweet

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