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Updated: Feb 24, 2026

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分析技術と機械学習に基づく漢方薬の品質管理:現状の進歩と将来の展望
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
まとめ
現代の分析技術と機械学習(ML)の組み合わせは、漢方薬の品質管理を大幅に向上させる。このアプローチは、地理的起源の追跡、混入検出、種同定を強化し、世界的な受容を促進する。
科学分野:
- 分析化学
- 計算生物学
- 薬学
背景:
- 漢方薬(HM)は、近代化と世界的な受容のために堅牢な品質管理(QC)を必要とする。
- 現在のHMのQCシステムは、標準化と包括的な評価において課題に直面している。
研究 の 目的:
- 現代の分析技術と機械学習(ML)の統合をレビューし、HM QCの進歩に役立てる。
- 過去10年間の地理的起源追跡、混入検出、種同定への応用に着目する。
主な方法:
- 過去10年間の研究に関する体系的な文献レビュー。
- 赤外線(IR)、質量分析(MS)、核磁気共鳴(NMR)などの技術とMLアルゴリズムを統合した研究の分析。
主要な成果:
- MLモデルは、分析技術からの複雑な化学データを効果的に解読する。
- 予測的かつ全体的な品質評価フレームワークが確立され、単一マーカーの限界を克服する。
- HMの起源追跡、混入検出、種同定への応用が実証されている。
結論:
- この統合は、HM QCにおける現在の研究状況を包括的に概観する。
- このレビューは、HMの将来の標準化された、データ駆動型、インテリジェントな品質保証戦略のための基礎的な参照として機能する。
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