{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T17:01:18Z","timestamp":1783098078926,"version":"3.54.6"},"reference-count":33,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T00:00:00Z","timestamp":1748476800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82130111"],"award-info":[{"award-number":["82130111"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["analyticalsciencejournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Phytochemical Analysis"],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Fruit\u2010derived medicinal materials (FDMM) are extensively utilized in daily life, yet the market is beset by substantial variety confusion, which undermines consumer rights and well\u2010being. Consequently, accurate identification of these materials is essential for guaranteeing their quality, effectiveness, and safety.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>This study aimed to combine attenuated total reflection\u2010Fourier transform infrared spectroscopy (ATR\u2010FTIR) and machine learning (ML) techniques to differentiate and identify 55 kinds of FDMM.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>A total of 861 sample batches were collected, with 721 allocated for model establishment and 140 for independent validation. A PLS\u2010DA model alongside nine machine learning algorithms\u2014namely support vector machine (SVM), tree, K\u2010nearest neighbor (KNN), discriminant, ensemble, support vector machine kernel (SVMK), logistic regression kernel (LRK), naive Bayes (NB), and neural network (NN)\u2014were constructed. Considering both accuracy and computational efficiency, the optimal model was selected and evaluated in terms of its accuracy, precision, recall, and F1\u2010score. The optimal model was further validated using 140 newly collected samples to ensure its long\u2010term stability after several months.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The application of ATR\u2010FTIR 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/pca.3545","type":"journal-article","created":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T06:05:24Z","timestamp":1748498724000},"page":"1790-1802","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["ATR\u2010FTIR Coupled With Machine Learning Provides a Fast Method for Identifying and Distinguishing 55 Varieties of Fruit\u2010Derived Medicinal Materials"],"prefix":"10.1002","volume":"36","author":[{"given":"Wen\u2010jie","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Chinese Materia Medica Nanjing University of Chinese Medicine  Nanjing China"},{"name":"Zhongshan Institute for Drug Discovery Shanghai Institute of Materia Medica, Chinese Academy of Science  Zhongshan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ya\u2010ling","family":"An","sequence":"additional","affiliation":[{"name":"Zhongshan Institute for Drug Discovery Shanghai Institute of Materia Medica, Chinese Academy of Science  Zhongshan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chun\u2010qian","family":"Song","sequence":"additional","affiliation":[{"name":"Zhongshan Institute for Drug Discovery Shanghai Institute of Materia Medica, Chinese Academy of Science  Zhongshan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu\u2010shi","family":"Huang","sequence":"additional","affiliation":[{"name":"Zhongshan Institute for Drug Discovery Shanghai Institute of Materia Medica, Chinese Academy of Science  Zhongshan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li\u2010jie","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Chinese Materia Medica Nanjing University of Chinese Medicine  Nanjing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kang\u2010nan","family":"Liu","sequence":"additional","affiliation":[{"name":"Zhongshan Institute for Drug Discovery Shanghai Institute of Materia Medica, Chinese Academy of Science  Zhongshan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen\u2010wei","family":"Li","sequence":"additional","affiliation":[{"name":"Zhongshan Institute for Drug Discovery Shanghai Institute of Materia Medica, Chinese Academy of Science  Zhongshan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7784-8100","authenticated-orcid":false,"given":"Xiao\u2010kang","family":"Liu","sequence":"additional","affiliation":[{"name":"Zhongshan Institute for Drug Discovery Shanghai Institute of Materia Medica, Chinese Academy of Science  Zhongshan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dai\u2010di","family":"Zhang","sequence":"additional","affiliation":[{"name":"Zhongshan Institute for Drug Discovery Shanghai Institute of Materia Medica, Chinese Academy of Science  Zhongshan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9411-6358","authenticated-orcid":false,"given":"De\u2010an","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Chinese Materia Medica Nanjing University of Chinese Medicine  Nanjing China"},{"name":"Zhongshan Institute for Drug Discovery Shanghai Institute of Materia Medica, Chinese Academy of Science  Zhongshan China"},{"name":"Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Research Center for TCM Standardization Technology Shanghai Institute of Materia Medica, Chinese Academy of Sciences  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,5,29]]},"reference":[{"key":"e_1_2_6_2_1","volume-title":"Pharmacopoeia of the People's Republic of China","author":"C.o.C. 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