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Simultaneous determination of six index constituents and comparative analysis of four ethnomedicines from genus Gentiana using a UPLC‐UV‐MS method
Authors:Yu Pan  Tao Shen  Ji Zhang  Yan‐Li Zhao  Yuan‐Zhong Wang  Wan‐Yi Li
Affiliation:1. Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming, China;2. College of Traditional Chinese Medicine, Yunnan University of Traditional Chinese Medicine, Kunming, China;3. College of Resources and Environment, Yuxi Normal University, Yuxi, China
Abstract:Many species from genus Gentiana (Gentianaceae) have a long history of applications as folk medicines in the world. A simple rapid UPLC‐UV‐MS/MS method has been developed and validated for the simultaneous determination of six index constituents (gentiopicroside, swertiamarin, loganic acid, sweroside, mangiferin and ferulic acid) from the four ethnomedicines (G. rigescens Franch. ex Hemsl., G. rhodantha Franch. ex Hemsl., G. scabra Bunge and G. farreri Balf. f.). The UPLC analysis was performed on Shim‐Pack XR‐ODS III (150 × 2.0 mm, 2.2 µm). The mobile phase was consisted of acetonitrile–0.1% formic acid water using gradient elution. The wavelength 242 nm was chose for the four iridoids as well as mangiferin and 320 nm was set for ferulic acid. Mass spectrometry was applied for identification and quantification for analytes with low concentration. All the regression equations revealed a good linear relationship (R2 > 0.9993). Accuracy and precision were all within the required limits. The chromatogram fingerprints analysis combined with principal component analysis showed the similarity values of the four species were <0.788 while the similarity values of G. scabra Bunge and G. rigescens Franch. ex Hemsl. were >0.993, which provided reasonable foundation for utilization and clinical application of the four ethnomedicines. This developed method appears to be a useful tool for quality control of the four ethnomedicines. Copyright © 2014 John Wiley & Sons, Ltd.
Keywords:UPLC‐UV‐MS/MS  ethnomedicines  Gentiana  fingerprints  principal component analysis
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