Characterization and feature selection of volatile metabolites in Yangxian pigmented rice varieties through GC-MS and machine learning algorithms
These results implied that GC-MS-based metabolomics combined with random forest might be effective for extracting key features among different pigmented rice varieties.
Authors
- Jingzhang Geng (ORCID: https://orcid.org/0000-0002-3470-2524)
- Wengang Jin (ORCID: https://orcid.org/0000-0002-9838-3905)
- Ruonan Dong
- Fei Pan (ORCID: https://orcid.org/0000-0001-9898-1238)
- Wen‐Hao Su (ORCID: https://orcid.org/0000-0003-1745-4722)
- A.M. Abd El‐Aty (ORCID: https://orcid.org/0000-0001-6596-7907)
- Meng Zhang (ORCID: https://orcid.org/0000-0002-2253-4025)
- Li Xi (ORCID: https://orcid.org/0000-0001-9148-3340)
- Ruichang Gao
- Kaiqi Cheng
Institutions
- Jiangsu University (CN)
- Cairo University (EG)
- Shaanxi University of Technology (CN)
- Jiangsu University of Science and Technology (CN)
- Chinese Academy of Agricultural Sciences (CN)
- Institute of Apiculture Research (CN)
- Atatürk University (TR)
Publication Details
- Journal
- Frontiers in Nutrition
- Published
- 2025-05-20
- DOI
- https://doi.org/10.3389/fnut.2025.1598875
- Citations
- 3
- Primary Topic
- GABA and Rice Research
- Type
- article
- Field-Weighted Citation Impact
- 5.25