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An quality evaluation method based on three-dimensional integration and machine learning: Advanced data processing.

Jianglei Zhang1, Yu Ren1, Jin Zeng1

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

Journal of Chromatography. A
|March 5, 2025
PubMed
Summary
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This study introduces a new method for traditional Chinese medicine (TCM) quality control using 3D data processing and machine learning. This approach enhances HPLC-DAD analysis accuracy and efficiency for better TCM quality assessment.

Area of Science:

  • Analytical Chemistry
  • Computational Chemistry
  • Pharmacology

Background:

  • Traditional Chinese Medicine (TCM) quality control faces challenges in efficiency and accuracy.
  • High-Performance Liquid Chromatography with Diode Array Detection (HPLC-DAD) is crucial for TCM analysis.
  • Existing methods struggle with data complexity and batch-to-batch variability.

Purpose of the Study:

  • To develop an innovative approach for TCM quality evaluation.
  • To enhance the efficiency and accuracy of HPLC-DAD data analysis.
  • To integrate 3D data processing and machine learning for robust TCM quality assessment.

Main Methods:

  • Three-dimensional (3D) data integration to simplify multi-dimensional HPLC-DAD signals.
  • Dynamic Time Warping (DTW) and Correlation Optimized Warping (COW) for retention time drift correction.
Keywords:
3D integrationBinary evaluation systemMachine learningRetention time correctionTCM quality control

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  • Binary Evaluation System (BES) with macro qualitative (Sm) and quantitative (Pm) similarity.
  • Machine learning models: Multiple Linear Regression (MLR), Decision Tree Regression (DTR), Random Forest Regression (RFR).

    • Main Results:

    • Simplified two-dimensional data from 3D integration for precise component quantification.
    • Effective alignment of chromatographic peak shapes, resolving retention time drift.
    • Demonstrated prediction error of ±0.2% for Baicalin content in Scutellaria baicalensis samples.
    • Enhanced data processing efficiency and reduced experimental resource consumption.

    Conclusions:

    • The integrated 3D data processing and machine learning approach provides a robust foundation for TCM quality assessment.
    • This method significantly improves automation and accuracy in TCM quality control.
    • Confirms broad applicability for modernizing TCM quality evaluation systems.