Application of spectroscopic technology with machine learning in Chinese herbs from seeds to medicinal materials: The case of genus Paris

文献类型： 外文期刊

第一作者： Feng, Yangna

作者： Feng, Yangna;Zhu, Xinyan;Wang, Yuanzhong;Feng, Yangna

作者机构：

关键词： Medicinal herbs; Genus Paris; Spectroscopic technology; Quality detection; Supply chain; Machine learning

期刊名称：JOURNAL OF PHARMACEUTICAL ANALYSIS （ 影响因子：8.9； 五年影响因子：8.4 ）

ISSN： 2095-1779

年卷期： 2025 年 15 卷 2 期

页码：

收录情况： SCI

摘要： To ensure the safety and efficacy of Chinese herbs, it is of great significance to conduct rapid quality detection of Chinese herbs at every link of their supply chain. Spectroscopic technology can reflect the overall chemical composition and structural characteristics of Chinese herbs, with the multi-component and multitarget characteristics of Chinese herbs. This review took the genus Paris as an example, and applications of spectroscopic technology with machine learning (ML) in supply chain of the genus Paris from seeds to medicinal materials were introduced. The specific contents included the confirmation of germplasm resources, identification of growth years, cultivar, geographical origin, and original processing and processing methods. The potential application of spectroscopic technology in genus Paris was pointed out, and the prospects of combining spectroscopic technology with blockchain were proposed. The summary and prospects presented in this paper will be beneficial to the quality control of the genus Paris in all links of its supply chain, so as to rationally use the genus Paris resources and ensure the safety and efficacy of medication. (c) 2024 The Author(s). Published by Elsevier B.V. on behalf of Xi'an Jiaotong University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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