Precise classification of traditional Chinese medicine sources using intelligent fusion of hyperspectral imaging-mass spectrometry data combined with machine learning: A case study of American ginseng.

Journal: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
PMID:

Abstract

The application of artificial intelligence in traditional Chinese medicine (TCM) has become a hot topic in the scientific community. American ginseng (AG), a perennial herb with a rich history, is widely utilized in clinical settings due to its diverse pharmacological activities and nutritional value. However, the quality of AG in the market is often compromised by the presence of similar-looking adulterants from different regions. Rapid and precise identification of its origin is crucial for consumers. This study proposes a novel approach, employing a Mid-Level-Fusion method that combines hyperspectral imaging (HSI) and ultra performance liquid chromatography-quadrupole linear ion trap mass spectrometry (UPLC-QTRAP-MS/MS) techniques to successfully identify origins of AG. Firstly, the 1D-Gradient-weighted class activation mapping (1D-GradCAM) algorithm was utilized for feature selection on HSI data, visualizing wavelengths contributing significantly to classification results and using the 1D-GradCAM algorithm, the spectral features were reduced from 510 to 91, achieving 105 % of the performance of the full-wavelength model. Simultaneously, redundant data in UPLC-QTRAP-MS/MS were eliminated using Cars-PLS, reducing the number of indicator components from 23 to 11. Subsequently, a Mid-Level Fusion matrix was generated based on the filtered HSI and UPLC-QTRAP-MS/MS data to establish an AG origin tracing model, achieving a detection accuracy of up to 96.15 %. Finally, the established HSI-UPLC-QTRAP-MS/MS-Mid-Level-Fusion model enabled pixel-level recognition of origin tracing. In conclusion, HSI combined with UPLC-QTRAP-MS/MS Mid-Level-Fusion presents a feasible method for tracing AG origins, playing a crucial role in quality control at the source in TCM production.

Authors

  • Hui Zhang
    Department of Pulmonary Vessel and Thrombotic Disease, Sixth Medical Center, Chinese PLA General Hospital, Beijing, China.
  • Hongxu Zhang
    Department of Breast Surgery, Affiliated Hospital of Chengde Medical University, Chengde 067000, Hebei, China.
  • ShouRong Wu
    State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China.
  • Xingchu Gong
    Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.
  • Jieqiang Zhu
    College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310014, China.
  • Jizhong Yan
    College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou, China.
  • Yong Jiang
    Department of Pathology West China Hospital Sichuan University Chengdu China.