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Exploring the chemical compositions of Fufang Yinhua Jiedu granules based on ultra-high-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry combined with multistage intelligent data annotation strategy

四极飞行时间 质谱法 色谱法 四极 化学 飞行时间质谱 注释 分析化学(期刊) 材料科学 计算机科学 电喷雾电离 人工智能 物理 有机化学 电离 离子 原子物理学
作者
Lan Yao,Xiu Wang,Yi Nan,Haizhen Liang,Meiyan Wang,Juan Song,Xiaojuan Chen,Baiping Ma
出处
期刊:Journal of Chromatography A [Elsevier BV]
卷期号:1728: 465010-465010 被引量:5
标识
DOI:10.1016/j.chroma.2024.465010
摘要

Fufang Yinhua Jiedu granules (FYJG) is a Traditional Chinese Medicine (TCM) compound formulae preparation comprising ten herbal drugs, which has been widely used for the treatment of influenza with wind-heat type and upper respiratory tract infections. However, the phytochemical constituents of FYJG have rarely been reported, and its constituent composition still needs to be elucidated. The complexity of the natural ingredients of TCMs and the diversity of preparations are the major obstacles to fully characterizing their constituents. In this study, an innovative and intelligent analysis strategy was built to comprehensively characterize the constituents of FYJG and assign source attribution to all components. Firstly, a simple and highly efficient ultra-high-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (UHPLC-QTOF MSE) method was established to analyze the FYJG and ten single herbs. High-accuracy MS/MS data were acquired under two collision energies using high-definition MSE in the negative and positive modes. Secondly, a multistage intelligent data annotation strategy was developed and used to rapidly screen out and identify the compounds of FYJG, which was integrated with various online software and data processing platforms. The in-house chemical library of 2949 compounds was created and operated in the UNIFI software to enable automatic peak annotation of the MSE data. Then, the acquired MS data were processed by MS-DIAL, and a feature-based molecular networking (FBMN) was constructed on the Global Natural Product Social Molecular Networking (GNPS) to infer potential compositions of FYJG by rapidly classifying and visualizing. It was simultaneously using the MZmine software to recognize the source attribution of ingredients. On this basis, the unique chemical categories and characteristics of herbaceous plant species are utilized further to verify the accuracy of the source attribution of multi-components. This comprehensive analysis successfully identified or tentatively characterized 279 compounds in FYJG, including flavonoids, phenolic acids, coumarins, saponins, alkaloids, lignans, and phenylethanoids. Notably, twelve indole alkaloids and four organic acids from Isatidis Folium were characterized in this formula for the first time. This study demonstrates a potential superiority to identify compounds in complex TCM formulas using high-definition MSE and computer software-assisted structural analysis tools, which can obtain high-quality MS/MS spectra, effectively distinguish isomers, and improve the coverage of trace components. This study elucidates the various components and sources of FYJG and provides a theoretical basis for its further clinical development and application.
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