ArticleCurrent research in food science2022
Informed classification of sweeteners/bitterants compounds via explainable machine learning.
Article in Current research in food science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Insight into the influence of various cultivation regions on the identification of metabolites from Capsella bursa-pastoris via a clustering algorithm.Scientific reports · 2025Article
- Computational screening of umami tastants using deep learning.Molecular diversity · 2025Article
- Genome-wide association study of the taste and hedonic ratings of the low-calorie sweetener acesulfame potassium.Scientific reports · 2025Observational
- A systematic review of data and models for predicting food flavor and texture.Current research in food science · 2025Review
- AI-driven prediction of bitterness and sweetness and analysis of receptor interactions.Current research in food science · 2025Article
- Advances, opportunities, and challenges in methods for interrogating the structure activity relationships of natural products.Natural product reports · 2024Review
- Predicting multiple taste sensations with a multiobjective machine learning method.NPJ science of food · 2024Article
- Explainable machine learning for predicting the geographical origin of Chinese Oysters via mineral elements analysis.Current research in food science · 2024Article
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6 authors.
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Abstract
Perception of taste is an emergent phenomenon arising from complex molecular interactions between chemical compounds and specific taste receptors. Among all the taste perceptions, the dichotomy of sweet and bitter tastes has been the subject of several machine learning studies for classification purposes. While previous studies have provided accurate sweeteners/bitterants classifiers, there is ample scope to enhance these models by enriching the understanding of the molecular basis of bitter-sweet tastes. Towards these goals, our study focuses on the development and testing of several machine learning strategies coupled with the novel SHapley Additive exPlanations (SHAP) for a rational sweetness/bitterness classification. This allows the identification of the chemical descriptors of interest by allowing a more informed approach toward the rational design and screening of sweeteners/bitterants. To support future research in this field, we make all datasets and machine learning models publicly available and present an easy-to-use code for bitter-sweet taste prediction.
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