Rapid Raman spectroscopy analysis assisted with machine learning: a case study on Radix Bupleuri
Among these machine learning models, the typical LDA-SVM models, consistent with the high-performance liquid chromatography results, demonstrate great performance and stability. We envision that this rapid classification and regression technique can be extended to predictions for other herbs. © 2024 Society of Chemical Industry.
Authors
- Xudong Yang (ORCID: https://orcid.org/0000-0002-4688-4935)
- Zhengyong Zhang (ORCID: https://orcid.org/0000-0002-5283-6462)
- Yinsheng Zhang (ORCID: https://orcid.org/0000-0003-4971-8809)
- Haiyan Wang (ORCID: https://orcid.org/0000-0001-7918-6765)
- Fangjie Guo (ORCID: https://orcid.org/0009-0000-0146-2651)
- Shuren Liu (ORCID: https://orcid.org/0009-0004-6045-0078)
Institutions
- Nanjing University of Finance and Economics (CN)
- Zhejiang Shuren University (CN)
- Zhejiang Gongshang University (CN)
Publication Details
- Journal
- Journal of the Science of Food and Agriculture
- Published
- 2024-11-08
- DOI
- https://doi.org/10.1002/jsfa.14012
- Citations
- 2
- Primary Topic
- Spectroscopy Techniques in Biomedical and Chemical Research
- Type
- article
- Field-Weighted Citation Impact
- 1.32
Funders
- National Natural Science Foundation of China
- Ministry of Science and Technology of the People's Republic of China