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Focusing on research from the past 10\u2009years, we highlight groundbreaking applications in three pivotal areas: geographical origin tracing, adulteration detection, and species identification of HMs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A comprehensive review of the literature from the past 10\u2009years was conducted, focusing on geographical origin tracing, adulteration detection, and species identification of HMs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Providing a structured overview of analytical technologies and ML algorithmic principles demonstrates how ML models decode complex, high\u2010dimensional chemical data to establish predictive and holistic quality assessment frameworks that transcend the limitations of single\u2010marker analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This synthesis maps the current research landscape and is a foundational reference for guiding future efforts toward standardized, data\u2010driven, and intelligent quality assurance in HMs.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/pca.70065","type":"journal-article","created":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T20:07:19Z","timestamp":1773000439000},"page":"488-506","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Quality Control of Herbal Medicine Based on Analytical Techniques and Machine Learning: Current Advances and Future Perspectives"],"prefix":"10.1002","volume":"37","author":[{"given":"Yihong","family":"Shen","sequence":"first","affiliation":[{"name":"School of Pharmacy and School of Ethnic Medicine Chengdu 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