基于分析技术和机器学习的草药质量控制:目前的进展和未来的前景
Yihong Shen1, Huan Du2, Fenglian Liu1
1School of Pharmacy and School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Phytochemical analysis : PCA
|February 23, 2026
概括
现代分析技术与机器学习 (ML) 结合,显著改善了草药质量控制. 这种方法增强了地理来源追踪,伪造检测和物种识别,以获得全球认可.
科学领域:
- 分析化学 分析化学
- 计算生物学 计算生物学
- 药理学是指药理学,即药理学是指药理学.
背景情况:
- 草药 (HM) 需要强有力的质量控制 (QC) 现代化和全球接受.
- 目前用于HM的质量控制系统在标准化和全面评估方面面临挑战.
研究的目的:
- 审查现代分析技术与机器学习 (ML) 的整合,以推进HM QC.
- 在过去的十年中,专注于地理来源追踪,伪造检测和物种识别方面的应用.
主要方法:
- 对过去10年的研究进行系统的文献综述.
- 分析将红外 (IR),质谱 (MS) 和核磁共振 (NMR) 等技术与ML算法集成的研究.
主要成果:
- 机器学习模型有效地从分析技术中解码复杂的化学数据.
- 建立了预测性和整体的质量评估框架,克服了单一标记者的局限性.
- 在追踪HM原产地,检测伪造物和识别物种方面有经验证明的应用.
结论:
- 综合提供了对HM QC当前研究格局的全面概述.
- 本综述作为未来标准化,数据驱动和智能化质量保证战略的基础参考,用于HMs.
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