ReviewEuropean food research and technology = Zeitschrift fur Lebensmittel-Untersuchung und -Forschung. A2022
A survey on computational taste predictors.
Review in European food research and technology = Zeitschrift fur Lebensmittel-Untersuchung und -Forschung. A, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed.
- Umamifusion: attention-enhanced multimodal fusion of graph and sequence features for umami peptide identification.Molecular diversity · 2026Article
- A chemical language model for molecular taste prediction.NPJ science of food · 2025Article
- Unravelling the human taste receptor interactome: machine learning and molecular modelling insights into protein-protein interactions.NPJ science of food · 2025Article
- AI-driven prediction of bitterness and sweetness and analysis of receptor interactions.Current research in food science · 2025Article
- A systematic review of data and models for predicting food flavor and texture.Current research in food science · 2025Review
- FlavorMiner: a machine learning platform for extracting molecular flavor profiles from structural data.Journal of cheminformatics · 2024Article
- Bitter peptide prediction using graph neural networks.Journal of cheminformatics · 2024Article
- Predicting multiple taste sensations with a multiobjective machine learning method.NPJ science of food · 2024Article
- Critical View on the Qualification of Electronic Tongues Regarding Their Performance in the Development of Peroral Drug Formulations with Bitter Ingredients.Pharmaceutics · 2024Review
- BitterMasS: Predicting Bitterness from Mass Spectra.Journal of agricultural and food chemistry · 2024Article
- Predicting and improving complex beer flavor through machine learning.Nature communications · 2024Article
- Exploring TAS2R46 biomechanics through molecular dynamics and network analysis.Frontiers in molecular biosciences · 2024Article
- A Comprehensive Comparative Analysis of Deep Learning Based Feature Representations for Molecular Taste Prediction.Foods (Basel, Switzerland) · 2023Article
- Toward a general and interpretable umami taste predictor using a multi-objective machine learning approach.Scientific reports · 2022Article
- Informed classification of sweeteners/bitterants compounds via explainable machine learning.Current research in food science · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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. Supplementary Information: The online version contains supplementary material available at 10.1007/s00217-022-04044-5.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.