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<p>Ciwujia tablet (CT) is extensively utilized in China to enhance sleep quality and manage cardiovascular and cerebrovascular disorders. This study presents a comprehensive strategy for evaluating the quality of CT from various manufacturers by integrating qualitative identification, quantitative analysis, and multivariate statistics. When identifying small molecules, traditional manual methods should be transformed into more efficient approaches that leverage software to facilitate complex analyses. Among the fragmentation software evaluated, MS-Finder distinguished itself as particularly effective. Following data acquisition through UHPLC-Orbitrap Fusion Tribrid mass spectrometry, the chemical characterization of the CT was performed using MS-DIAL, MS-Finder, and GNPS molecular networking techniques. In total, 80 compounds were identified in positive ion mode and 92 compounds in negative ion mode. Subsequently, parallel reaction monitoring (PRM) was used to analyze the eight key compounds identified in CT quantitatively. All eight components exhibited good linearity within their respective mass concentration ranges (<i>r</i> ≥ 0.99) and met the relevant requirements for precision, repeatability, and stability. Recovery rates ranged from 99.16% to 101.07%. Finally, multivariate statistical methods, such as hierarchical cluster analysis (HCA), principal component analysis (PCA), and orthogonal partial least squares discriminant analysis (OPLS-DA), were conducted using SIMCA 14.1 software to elucidate the differences among CT samples from ten manufacturers. The results indicate that the method established in this study can perform sensitive and efficient qualitative and quantitative analysis of the chemical components in CT. It is a promising approach for exploring compounds and provides new research directions and scientific support for future studies on quality control measures for CT.</p> Graphical Abstract <p></p>

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Qualitative and Quantitative Evaluations of Ciwujia Tablets: MS-DIAL, MS-FINDER, and GNPS-Assisted Chemical Characterization

  • 期刊论文

  • 发表日期：

    2025年12月4日

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  • Yan Liu,
  • Tiantian Wen,
  • Jianjia Zhang,
  • Wei Guan,
  • Chunhe Lin,
  • Kezhen Chen,
  • Yanying Li,
  • Qingshan Chen,
  • Lili Zhang,
  • Jiujiang Yan,
  • Haixue Kuang,
  • Bingyou Yang,
  • Hao Zhang

摘要

Ciwujia tablet (CT) is extensively utilized in China to enhance sleep quality and manage cardiovascular and cerebrovascular disorders. This study presents a comprehensive strategy for evaluating the quality of CT from various manufacturers by integrating qualitative identification, quantitative analysis, and multivariate statistics. When identifying small molecules, traditional manual methods should be transformed into more efficient approaches that leverage software to facilitate complex analyses. Among the fragmentation software evaluated, MS-Finder distinguished itself as particularly effective. Following data acquisition through UHPLC-Orbitrap Fusion Tribrid mass spectrometry, the chemical characterization of the CT was performed using MS-DIAL, MS-Finder, and GNPS molecular networking techniques. In total, 80 compounds were identified in positive ion mode and 92 compounds in negative ion mode. Subsequently, parallel reaction monitoring (PRM) was used to analyze the eight key compounds identified in CT quantitatively. All eight components exhibited good linearity within their respective mass concentration ranges (r ≥ 0.99) and met the relevant requirements for precision, repeatability, and stability. Recovery rates ranged from 99.16% to 101.07%. Finally, multivariate statistical methods, such as hierarchical cluster analysis (HCA), principal component analysis (PCA), and orthogonal partial least squares discriminant analysis (OPLS-DA), were conducted using SIMCA 14.1 software to elucidate the differences among CT samples from ten manufacturers. The results indicate that the method established in this study can perform sensitive and efficient qualitative and quantitative analysis of the chemical components in CT. It is a promising approach for exploring compounds and provides new research directions and scientific support for future studies on quality control measures for CT.

Graphical Abstract