ArticleRSC advances2021
Prediction of flavor and retention index for compounds in beer depending on molecular structure using a machine learning method.
Article in RSC advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- FlavorGPN: a graph neural network for multi-label molecular flavor prediction.Molecular diversity · 2026Article
- Machine learning for food flavor prediction and regulation: models, data integration, and future perspectives.Journal of advanced research · 2026Review
- Machine learning unveils three layers of food complexity.NPJ science of food · 2026Review
- 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
- Artificial intelligence and machine learning applications for cultured meat.Frontiers in artificial intelligence · 2024Review
- A machine learning method for juice human sensory hedonic prediction using electronic sensory features.Current research in food science · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In order to make a preliminary prediction of flavor and retention index (RI) for compounds in beer, this work applied the machine learning method to modeling depending on molecular structure. Towards this goal, the flavor compounds in beer from existing literature were collected. The database was classified into four groups as aromatic, bitter, sulfury, and others. The RI values on a non-polar SE-30 column and a polar Carbowax 20M column from the National Institute of Standards Technology (NIST) were investigated. The structures were converted to molecular descriptors calculated by molecular operating environment (MOE), ChemoPy and Mordred, respectively. By combining the pretreatment of the descriptors, machine learning models, including support vector machine (SVM), random forest (RF) and
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Registered trials
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