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Issue 40, 2017
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Detection of chemical constituents in Gegenqinlian decoction by ultra-high performance liquid chromatography coupled with Fourier transform ion cyclotron resonance mass spectrometry

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Abstract

Gegenqinlian decoction (GQLD) is one of the classic traditional Chinese medicines. The formula consists of four herbal medicines including Radix Puerariae lobatae, Radix Scutellariae, Coptidis Rhizoma, and Glycyrrhizae Radix et Rhizoma Praeparata Cum Melle. In this study, an ultra-high performance liquid chromatography combined with Fourier transform ion cyclotron resonance mass spectrometry method was employed to investigate the major constituents in GQLD. Liquid chromatographic separation was conducted on a HSS T3 column (2.1 mm × 100 mm, 1.8 μm) with a mobile phase consisting of acetonitrile and 0.1% formic acid–water at a flow rate of 0.30 mL min−1. Accurate mass measurements for molecular ions (below 2 ppm in most cases) and characteristic fragment ions could represent reliable identification criteria for these compounds. In total, 134 compounds in GQLD were identified or tentatively characterized mainly including flavonoids, alkaloids and triterpenoids. In conclusion, the UHPLC-FT-ICR-MS is a highly efficient technique to separate and identify constituents in the complex matrices of traditional Chinese medicine. These results could provide a basis for quality control and further in vivo research of GQLD.

Graphical abstract: Detection of chemical constituents in Gegenqinlian decoction by ultra-high performance liquid chromatography coupled with Fourier transform ion cyclotron resonance mass spectrometry

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Publication details

The article was received on 30 Aug 2017, accepted on 20 Sep 2017 and first published on 21 Sep 2017


Article type: Paper
DOI: 10.1039/C7AY02082A
Citation: Anal. Methods, 2017,9, 5890-5902
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    Detection of chemical constituents in Gegenqinlian decoction by ultra-high performance liquid chromatography coupled with Fourier transform ion cyclotron resonance mass spectrometry

    T. Liu, Y. Cui, X. Tian, S. Li, F. Han, B. Ji, Y. Zhao and Z. Yu, Anal. Methods, 2017, 9, 5890
    DOI: 10.1039/C7AY02082A

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