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相关实验视频

Updated: May 24, 2025

Evaluation of Left Ventricular Structure and Function using 3D Echocardiography
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基于三维集成和机器学习的质量评估方法:先进的数据处理.

Jianglei Zhang1, Yu Ren1, Jin Zeng1

  • 1School of Pharmacy, Shenyang Pharmaceutical University, Shenyang, Liaoning 110016, China.

Journal of chromatography. A
|March 5, 2025
PubMed
概括
此摘要是机器生成的。

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这项研究引入了使用3D数据处理和机器学习进行传统中医 (TCM) 质量控制的新方法. 这种方法提高了HPLC-DAD分析的准确性和效率,以更好地评估TCM的质量.

科学领域:

  • 分析化学 分析化学
  • 计算化学计算化学
  • 药理学 药理学是指药理学的学科.

背景情况:

  • 传统中医药 (TCM) 质量控制在效率和准确性方面面临挑战.
  • 使用二极管阵列检测 (HPLC-DAD) 的高性能液体染色学对于TCM分析至关重要.
  • 现有的方法在数据复杂性和批次对批次的可变性方面扎.

研究的目的:

  • 开发一种创新的方法来评估TCM的质量.
  • 为了提高HPLC-DAD数据分析的效率和准确性.
  • 整合3D数据处理和机器学习,以进行可靠的TCM质量评估.

主要方法:

  • 三维 (3D) 数据集成以简化多维HPLC-DAD信号.
  • 动态时间曲线 (DTW) 和相关性优化曲线 (COW) 用于保留时间漂移的纠正.
  • 二元评估系统 (BES) 具有宏观的定性 (Sm) 和定量 (Pm) 相似性.
  • 机器学习模型:多重线性回归 (MLR),决策树回归 (DTR),随机森林回归 (RFR).

主要成果:

  • 从3D集成中简化二维数据,用于精确的组件量化.
  • 有效地对准色谱峰形状,解决保留时间漂移.
关键词:
3D整合 3D整合二进制评估系统的二进制评估系统.机器学习是机器学习.保持时间的校正.在TCM质量控制的质量控制.

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  • 在Scutellaria baicalensis样本中的Baicalin含量显示的预测误差为±0.2%.
  • 提高数据处理效率和减少实验资源的消耗.

    • 结论:

    • 综合的3D数据处理和机器学习方法为TCM质量评估提供了坚实的基础.
    • 这种方法显著提高了TCM质量控制的自动化和准确性.
    • 证实了TCM质量评估系统现代化的广泛适用性.