ArticleNPJ science of food2025
Machine learning-based prediction of volatile compounds profiles in Saccharomyces cerevisiae fermentation simulating canned meat.
Article in NPJ science of food, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Machine-learning-assisted screening of key flavor compounds in pumpkins.Journal of the science of food and agriculture · 2026Article
- Machine learning for food flavor prediction and regulation: models, data integration, and future perspectives.Journal of advanced research · 2026Review
- Artificial Intelligence Virtual Organoids (AIVOs).Bioactive materials · 2026Review
- Machine Learning in Transforming the Food Industry.Foods (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Aroma and precision fermentation converge in exciting ways, enabling the precise production of aromatic compounds. Precision fermentation employs engineered microorganisms to create and refine scents and aromas with high accuracy, allowing for customizable aromas and opening new possibilities for both culinary experiences and consumer products. Structured data on volatile compounds from canned meat and fermented products was compiled to train machine learning (ML) models aimed at predicting volatile compounds and simulating meat aroma in Saccharomyces cerevisiae. We proposed a framework encompassing data generation and preprocessing, feature selection, model construction, and evaluation. Principal Component Analysis ensured data quality control, while embedding-based feature selection identified key volatile compounds. A two-stage model was developed to quantify the importance of volatile compounds and predict meat aroma and the gradient-boosted decision trees (GBDT) model demonstrated optimal performance. Our study guides simulating meat aroma through fermentation, offering a promising approach for plant-based meat flavoring.
Identifiers
What OpenQuestion holds
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.