Machine learning-based prediction of volatile compounds profiles in Saccharomyces cerevisiae fermentation simulating canned meat
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 possibiliti...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-06-01
|
| Series: | npj Science of Food |
| Online Access: | https://doi.org/10.1038/s41538-025-00435-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850136944830316544 |
|---|---|
| author | Lin Du Shujie Wang Yongyan Chen Zhongxu Zhu Hai-Xi Sun Tsan-Yu Chiu |
| author_facet | Lin Du Shujie Wang Yongyan Chen Zhongxu Zhu Hai-Xi Sun Tsan-Yu Chiu |
| author_sort | Lin Du |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f9fa36e2d78e4f9aaa0ef12eb09de08a |
| institution | OA Journals |
| issn | 2396-8370 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Science of Food |
| spelling | doaj-art-f9fa36e2d78e4f9aaa0ef12eb09de08a2025-08-20T02:30:59ZengNature Portfolionpj Science of Food2396-83702025-06-019111310.1038/s41538-025-00435-6Machine learning-based prediction of volatile compounds profiles in Saccharomyces cerevisiae fermentation simulating canned meatLin Du0Shujie Wang1Yongyan Chen2Zhongxu Zhu3Hai-Xi Sun4Tsan-Yu Chiu5College of Life Sciences, University of Chinese Academy of SciencesBGIBGIBiomics Center, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesCollege of Life Sciences, University of Chinese Academy of SciencesBGIAbstract 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.https://doi.org/10.1038/s41538-025-00435-6 |
| spellingShingle | Lin Du Shujie Wang Yongyan Chen Zhongxu Zhu Hai-Xi Sun Tsan-Yu Chiu Machine learning-based prediction of volatile compounds profiles in Saccharomyces cerevisiae fermentation simulating canned meat npj Science of Food |
| title | Machine learning-based prediction of volatile compounds profiles in Saccharomyces cerevisiae fermentation simulating canned meat |
| title_full | Machine learning-based prediction of volatile compounds profiles in Saccharomyces cerevisiae fermentation simulating canned meat |
| title_fullStr | Machine learning-based prediction of volatile compounds profiles in Saccharomyces cerevisiae fermentation simulating canned meat |
| title_full_unstemmed | Machine learning-based prediction of volatile compounds profiles in Saccharomyces cerevisiae fermentation simulating canned meat |
| title_short | Machine learning-based prediction of volatile compounds profiles in Saccharomyces cerevisiae fermentation simulating canned meat |
| title_sort | machine learning based prediction of volatile compounds profiles in saccharomyces cerevisiae fermentation simulating canned meat |
| url | https://doi.org/10.1038/s41538-025-00435-6 |
| work_keys_str_mv | AT lindu machinelearningbasedpredictionofvolatilecompoundsprofilesinsaccharomycescerevisiaefermentationsimulatingcannedmeat AT shujiewang machinelearningbasedpredictionofvolatilecompoundsprofilesinsaccharomycescerevisiaefermentationsimulatingcannedmeat AT yongyanchen machinelearningbasedpredictionofvolatilecompoundsprofilesinsaccharomycescerevisiaefermentationsimulatingcannedmeat AT zhongxuzhu machinelearningbasedpredictionofvolatilecompoundsprofilesinsaccharomycescerevisiaefermentationsimulatingcannedmeat AT haixisun machinelearningbasedpredictionofvolatilecompoundsprofilesinsaccharomycescerevisiaefermentationsimulatingcannedmeat AT tsanyuchiu machinelearningbasedpredictionofvolatilecompoundsprofilesinsaccharomycescerevisiaefermentationsimulatingcannedmeat |