Issue 47, 2022

Rapid and comprehensive identification of chemical constituents in Mai-Luo-Shu-Tong pill by UHPLC-Q-Orbitrap HRMS combined with a data mining strategy

Abstract

Mai-Luo-Shu-Tong pill is an effective traditional Chinese medicine formula for the treatment of superficial thrombophlebitis, but it was insufficiently chemically scrutinized. In this study, the mass spectral data of Mai-Luo-Shu-Tong pill were acquired by ultra-high performance liquid chromatography coupled with Q Exactive hybrid Quadrupole-Orbitrap high resolution mass spectrometry. Then, a data mining strategy combining multiple data processing methods was used to identify chemical constituents in Mai-Luo-Shu-Tong pill by constructing a database of precursor ions and summarizing the mass spectral fragmentation behaviors. As a result, a total of 211 compounds including 70 flavonoids, 56 terpenoids, 37 phenolic acids and 48 others were identified in positive and negative ion modes. Among them, 66 compounds have passed comparison verification with reference standards, 145 compounds were identified based on the data mining strategy combining the characteristic cleavage behaviour of homologous compounds and fragment ions and 4 compounds were potentially new compounds. This study provides a database for quality evaluation and further study of Mai-Luo-Shu-Tong pill in vivo. Moreover, it provides a reference for the characterization of the chemical constituents of other traditional Chinese medicine formulae.

Graphical abstract: Rapid and comprehensive identification of chemical constituents in Mai-Luo-Shu-Tong pill by UHPLC-Q-Orbitrap HRMS combined with a data mining strategy

Supplementary files

Article information

Article type
Paper
Submitted
09 Sep 2022
Accepted
08 Nov 2022
First published
08 Nov 2022

Anal. Methods, 2022,14, 4990-5000

Rapid and comprehensive identification of chemical constituents in Mai-Luo-Shu-Tong pill by UHPLC-Q-Orbitrap HRMS combined with a data mining strategy

X. Zhang, Y. Chu, M. Wang, Y. Shi, L. Zuo, Z. Li, J. Liu, J. Kang, S. Du, B. Li, Z. Sun and X. Zhang, Anal. Methods, 2022, 14, 4990 DOI: 10.1039/D2AY01453J

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