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Qualitative and quantitative analysis of the chemical components in Yuquan capsules by using ultra‐performance liquid chromatography‐mass spectrometry.

  • Published In: Journal of Separation Science, 2023, v. 46, n. 16. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Wu, Fangfang; Tan, Zhien; Li, Taiping; Zhang, Mengli; He, Yanmei; Chen, Shimin; Yu, Pan; Ou, Min; Liu, Lingjie; Wang, Xijun 3 of 3

Abstract

The Yuquan capsules is a commonly used traditional Chinese Patent Medicine used for the treatment of diabetes mellitus. In this study, a high‐throughput analytical method for identifying the chemical composition of Yuquan capsules was established for the first time by using ultra‐performance liquid chromatography–quadrupole time of flight mass spectrometry. The data obtained were subjected to fragment analysis and this was combined with UNIFI processing of natural products. One‐hundred sixteen compounds were characterized from Yuquan capsules. Twelve of the bioactive compounds were quantitatively analyzed by ultra‐performance liquid chromatography‐tandem triple quadrupole mass spectrometry. This study was undertaken to obtain a comprehensive chemical profile analysis as well as to evaluate the overall quality of Yuquan capsules. The results will provide a reference for the quality evaluation of different Yuquan preparations. In addition, the data will enable basic pharmacodynamic research into these extensively used capsules. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Separation Science. 2023/08, Vol. 46, Issue 16, p1
  • Document Type:Article
  • Subject Area:Chemistry
  • Publication Date:2023
  • ISSN:1615-9306
  • DOI:10.1002/jssc.202300148
  • Accession Number:170394447
  • Copyright Statement:Copyright of Journal of Separation Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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