Wang Jin-Xuan, Yao Xia, Li Geng
Academician Workstation of Jiangxi University of Chinese Medicine Nanchang 330004, China.
Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences & Peking Union Medical College Beijing 100193, China.
Zhongguo Zhong Yao Za Zhi. 2024 Sep;49(17):4617-4629. doi: 10.19540/j.cnki.cjcmm.202400701.601.
Variety identification is the prerequisite and foundation for ensuring the quality of traditional Chinese medicine. The safety risks, legal risks, and regulatory trends underline the importance of traditional Chinese medicine varieties. With the industrialization of traditional Chinese medicine and the production of Chinese medicinal materials from wild growing to cultivation and breeding, new varieties of traditional Chinese medicine keep emerging. At the same time, facing the requirements of the entire process control of traditional Chinese medicine quality under the new situation, the current identification technology of traditional Chinese medicine is facing severe challenges. According to the current situation and evolution trend of technologies for traditional Chinese medicine identification, chemical components as the material basis for the effects of traditional Chinese medicine not only have rich characteristics but also can run through the entire quality formation and transmission process of traditional Chinese medicine. Identifying traditional Chinese medicine based on chemical components is the only way to achieve integrated medicine quality and whole-process quality control. The introduction of modern analytical instruments improves the scope and efficiency of obtaining chemical information of traditional Chinese medicine. Big data and artificial intelligence provide new opportunities for deeply interpreting the scientific connotation of chemical characteristic information of traditional Chinese medicine and comprehensively identifying traditional Chinese medicine based on chemical characteristics. Artificial intelligence can be employed to extract high-dimensional chemical information patterns from comprehensive chemical quantitative information collected from a large number of samples, remove the interference of noise information, and discover the chemical characteristics(components, quantity values, and relative proportions of components) that determine the variety. This approach can help to achieve integrated quality identification based on chemical characteristics, reflect the quality transmission process of traditional Chinese medicine, and achieve quality information traceability, providing support for building a whole-process quality control system of traditional Chinese medicine.
品种鉴定是保证中药质量的前提和基础。安全风险、法律风险以及监管趋势凸显了中药品种的重要性。随着中药产业化以及中药材生产从野生采集向种植养殖转变，中药新品种不断涌现。与此同时，面对新形势下中药质量全过程控制的要求，当前的中药鉴定技术面临严峻挑战。根据中药鉴定技术的现状和发展趋势，化学成分作为中药药效的物质基础，不仅具有丰富的特征，而且贯穿于中药质量形成和传递的全过程。基于化学成分鉴定中药是实现中药整体质量和全过程质量控制的必由之路。现代分析仪器的引入拓宽了获取中药化学信息的范围并提高了效率。大数据和人工智能为深入解读中药化学特征信息的科学内涵以及基于化学特征全面鉴定中药提供了新机遇。人工智能可用于从大量样本采集的综合化学定量信息中提取高维化学信息模式，去除噪声信息的干扰，发现决定品种的化学特征（成分、含量值以及成分相对比例）。这种方法有助于实现基于化学特征的整体质量鉴定，反映中药质量传递过程，实现质量信息追溯，为构建中药全过程质量控制体系提供支撑。
