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Pattern recognition: An effective tool for quality assessment of herbal medicine based on chemical information
Author(s) -
Wang Ye,
Zuo ZhiTian,
Wang YuanZhong
Publication year - 2021
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.3305
Subject(s) - chemometrics , traceability , computer science , quality (philosophy) , artificial intelligence , machine learning , data mining , philosophy , epistemology , software engineering
Herbal medicine has obtained great attention for its effective efficacy using its crude materials and patent medicines. It is believed that the efficacy is a synergetic action by several chemical components. To present a detailed overview of the usage of multivariate statistical techniques developed for analytical chemistry in quality assessment and origins traceability, we provided an extensive pragmatic and practical overview of these techniques for the quality control of these crude medicines. Two pattern recognition methods, unsupervised and supervised approaches, were interpreted using practical instances. Overall, the review briefly summarized common applications for location, species, harvesting time, processed production, manufacture, botanical part, and authenticity. Besides, we focused on data pretreatment and fusion strategies listing recently published literature to provide a detailed reference to choose the most appropriate statistical method and fusion strategies. Actual applications have proved chemometrics as an effective and rapid tool for the quality control of herbal medicine.