中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 上海药物研究所
  3. 中国科学院上海药物研究所
ATR-FTIR Coupled With Machine Learning Provides a Fast Method for Identifying and Distinguishing 55 Varieties of Fruit-Derived Medicinal Materials

文献类型：期刊论文

作者Zhao, Wen-jie2,3; An, Ya-ling2; Song, Chun-qian2; Huang, Yu-shi2; Zhang, Li-jie3; Liu, Kang-nan2; Li, Zhen-wei2; Liu, Xiao-kang2; Zhang, Dai-di2; Guo, De-an1,2,3
刊名PHYTOCHEMICAL ANALYSIS
出版日期2025-05-29
页码13
关键词ATR-FTIR fruit-derived medicinal materials machine learning species discrimination
ISSN号0958-0344
DOI10.1002/pca.3545
通讯作者Guo, De-an(daguo@simm.ac.cn)
英文摘要IntroductionFruit-derived medicinal materials (FDMM) are extensively utilized in daily life, yet the market is beset by substantial variety confusion, which undermines consumer rights and well-being. Consequently, accurate identification of these materials is essential for guaranteeing their quality, effectiveness, and safety.ObjectivesThis study aimed to combine attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) and machine learning (ML) techniques to differentiate and identify 55 kinds of FDMM.Materials and MethodsA total of 861 sample batches were collected, with 721 allocated for model establishment and 140 for independent validation. A PLS-DA model alongside nine machine learning algorithms-namely support vector machine (SVM), tree, K-nearest neighbor (KNN), discriminant, ensemble, support vector machine kernel (SVMK), logistic regression kernel (LRK), naive Bayes (NB), and neural network (NN)-were constructed. Considering both accuracy and computational efficiency, the optimal model was selected and evaluated in terms of its accuracy, precision, recall, and F1-score. The optimal model was further validated using 140 newly collected samples to ensure its long-term stability after several months.ResultsAmong the 10 classification models, the KNN model showed exceptional classification capability, with all evaluation metric exceeding 0.98. The KNN model was validated by the new 140 samples with a prediction accuracy of 85.7%, confirming its capability in distinguishing most FDMM.ConclusionThe 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.
WOS关键词INFRARED-SPECTROSCOPY
资助项目National Natural Science Foundation of China ; Qi-Huang Chief Scientist Project of National Administration of Traditional Chinese Medicine ; [82130111]
WOS研究方向Biochemistry & Molecular Biology ; Plant Sciences ; Chemistry
语种英语
WOS记录号WOS:001497999600001
出版者WILEY
源URL[http://119.78.100.183/handle/2S10ELR8/318267]  
专题中国科学院上海药物研究所
通讯作者Guo, De-an
作者单位1.Chinese Acad Sci, Shanghai Inst Mat Med, Natl Engn Res Ctr TCM Standardizat Technol, Shanghai Res Ctr Modernizat Tradit Chinese Med, Shanghai, Peoples R China
2.Chinese Acad Sci, Zhongshan Inst Drug Discovery, Shanghai Inst Mat Med, Zhongshan, Peoples R China
3.Nanjing Univ Chinese Med, Sch Chinese Mat Med, Nanjing, Peoples R China
推荐引用方式
GB/T 7714		 Zhao, Wen-jie,An, Ya-ling,Song, Chun-qian,et al. ATR-FTIR Coupled With Machine Learning Provides a Fast Method for Identifying and Distinguishing 55 Varieties of Fruit-Derived Medicinal Materials[J]. PHYTOCHEMICAL ANALYSIS,2025:13.
APA Zhao, Wen-jie.,An, Ya-ling.,Song, Chun-qian.,Huang, Yu-shi.,Zhang, Li-jie.,...&Guo, De-an.(2025).ATR-FTIR Coupled With Machine Learning Provides a Fast Method for Identifying and Distinguishing 55 Varieties of Fruit-Derived Medicinal Materials.PHYTOCHEMICAL ANALYSIS,13.
MLA Zhao, Wen-jie,et al."ATR-FTIR Coupled With Machine Learning Provides a Fast Method for Identifying and Distinguishing 55 Varieties of Fruit-Derived Medicinal Materials".PHYTOCHEMICAL ANALYSIS (2025):13.

入库方式： OAI收割

来源：上海药物研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。