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        Qualitative and quantitative analysis of the saponins in Panax notoginseng leaves using ultra-performance liquid chromatography coupled with time-of-flight tandem mass spectrometry and high performance liquid chromatography coupled with UV detector

        Liu, Fang,Ma, Ni,He, Chengwei,Hu, Yuanjia,Li, Peng,Chen, Meiwan,Su, Huanxing,Wan, Jian-Bo The Korean Society of Ginseng 2018 Journal of Ginseng Research Vol.42 No.2

        Background: Panax notoginseng leaves (PNL) exhibit extensive activities, but few analytical methods have been established to exclusively determine the dammarane triterpene saponins in PNL. Methods: Ultra-performance liquid chromatography coupled with time-of-flight mass spectrometry (UPLC/Q-TOF MS) and HPLC-UV methods were developed for the qualitative and quantitative analysis of ginsenosides in PNL, respectively. Results: Extraction conditions, including solvents and extraction methods, were optimized, which showed that ginsenosides Rc and Rb3, the main components of PNL, are transformed to notoginsenosides Fe and Fd, respectively, in the presence of water, by removing a glucose residue from position C-3 via possible enzymatic hydrolysis. A total of 57 saponins were identified in the methanolic extract of PNL by UPLC/Q-TOF MS. Among them, 19 components were unambiguously characterized by their reference substances. Additionally, seven saponins of PNL-ginsenosides Rb1, Rc, Rb2, and Rb3, and notoginsenosides Fc, Fe, and Fd-were quantified using the HPLC-UV method after extraction with methanol. The separation of analytes, particularly the separation of notoginsenoside Fc and ginsenoside Rc, was achieved on a Zorbax ODS C8 column at a temperature of $35^{\circ}C$. This developed HPLC-UV method provides an adequate linearity ($r^2$ > 0.999), repeatability (relative standard deviation, RSD < 2.98%), and inter- and intraday variations (RSD < 4.40%) with recovery (98.7-106.1%) of seven saponins concerned. This validated method was also conducted to determine seven components in 10 batches of PNL. Conclusion: These findings are beneficial to the quality control of PNL and its relevant products.

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