ATR‐FTIR Coupled With Machine Learning Provides a Fast Method for Identifying and Distinguishing 55 Varieties of Fruit‐Derived Medicinal Materials
The application of ATR-FTIR technology combined with the robust classification capabilities of ML models enabled rapid and accurate differentiation and identification of 55 FDMM, which contributed to ensuring their quality.
Authors
- Xiaoyu Liu (ORCID: https://orcid.org/0000-0003-3385-4726)
- Yang Huang (ORCID: https://orcid.org/0000-0002-1496-0972)
- Yaling An
- Christina Soyoung Song (ORCID: https://orcid.org/0000-0003-2031-5694)
- Lijie Zhang (ORCID: https://orcid.org/0009-0007-5515-9376)
- Dongping Zhang (ORCID: https://orcid.org/0000-0001-9825-1411)
- Zhenwei Li (ORCID: https://orcid.org/0000-0002-3260-2642)
- Wenjie Zhao (ORCID: https://orcid.org/0009-0002-0759-1192)
- De‐an Guo (ORCID: https://orcid.org/0009-0005-9411-6358)
- Kezhuang Liu
Institutions
- Nanjing University of Chinese Medicine (CN)
- Chinese Academy of Sciences (CN)
- Shanghai Innovative Research Center of Traditional Chinese Medicine (CN)
- Shanghai Institute of Materia Medica (CN)
Publication Details
- Journal
- Phytochemical Analysis
- Published
- 2025-05-29
- DOI
- https://doi.org/10.1002/pca.3545
- Citations
- 1
- Primary Topic
- Spectroscopy and Chemometric Analyses
- Type
- article
- Field-Weighted Citation Impact
- 0.75
Funders
- National Natural Science Foundation of China