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基于 UPLC-离子淌度-质谱联用和 Kendrick 质量亏损滤波数据挖掘的参芪扶正注射液的鉴定。

Authentication of Shenqi Fuzheng Injection via UPLC-Coupled Ion Mobility-Mass Spectrometry and Chemometrics with Kendrick Mass Defect Filter Data Mining.

机构信息

National Engineering Research Center of TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.

School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing 210029, China.

出版信息

Molecules. 2022 Jul 24;27(15):4734. doi: 10.3390/molecules27154734.

Abstract

Nearly 5% of the Shenqi Fuzheng Injection's dry weight comes from the secondary metabolites of and . However, the chemical composition of these metabolites is still vague, which hinders the authentication of Shenqi Fuzheng Injection (SFI). Ultra-high performance liquid chromatography with a charged aerosol detector was used to achieve the profiling of these secondary metabolites in SFI in a single chromatogram. The chemical information in the chromatographic profile was characterized by ion mobility and high-resolution mass spectrometry. Polygonal mass defect filtering (PMDF) combined with Kendrick mass defect filtering (KMDF) was performed to screen potential secondary metabolites. A total of 223 secondary metabolites were characterized from the SFI fingerprints, including 58 flavonoids, 71 saponins, 50 alkaloids, 30 polyene and polycynes, and 14 other compounds. Among them, 106 components, mainly flavonoids and saponins, are contributed by , while 54 components, mainly alkaloids and polyene and polycynes, are contributed by , with 33 components coming from both herbs. There were 64 components characterized using the KMDF method, which increased the number of characterized components in SFI by 28.70%. This study provides a solid foundation for the authentification of SFIs and the analysis of its chemical composition.

摘要

参芪扶正注射液干重的近 5%来自 和 的次级代谢物。然而,这些代谢物的化学成分仍然模糊不清,这阻碍了参芪扶正注射液(SFI)的鉴定。采用带电荷气溶胶检测器的超高效液相色谱法在单个色谱图中实现了 SFI 中这些次级代谢物的分析。通过离子淌度和高分辨率质谱对色谱图中的化学信息进行了表征。采用多边形质量缺陷过滤(PMDF)结合 Kendrick 质量缺陷过滤(KMDF)对潜在的次级代谢物进行筛选。从 SFI 指纹图谱中鉴定出 223 种次级代谢物,包括 58 种黄酮类、71 种皂苷、50 种生物碱、30 种多烯和多炔以及 14 种其他化合物。其中,106 种成分,主要是黄酮类和皂苷,来源于 ,54 种成分,主要是生物碱和多烯和多炔,来源于 ,33 种成分来自两种草药。使用 KMDF 方法鉴定了 64 种成分,使 SFI 中鉴定的成分数量增加了 28.70%。这项研究为 SFI 的鉴定和化学成分分析提供了坚实的基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/801e/9330873/4aeb8c751f15/molecules-27-04734-g001.jpg

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