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]
卷期号:1728: 465010-465010 被引量:10
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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
仔仔发布了新的文献求助10
1秒前
MildW发布了新的文献求助10
2秒前
4秒前
火星上小蚂蚁完成签到,获得积分20
4秒前
riverhj完成签到,获得积分20
6秒前
SciGPT应助栗子采纳,获得10
7秒前
小马哥完成签到,获得积分10
7秒前
害羞外套发布了新的文献求助20
8秒前
9秒前
呵呵壕应助riverhj采纳,获得10
9秒前
萱瑄爸爸完成签到,获得积分10
9秒前
夜安完成签到 ,获得积分10
9秒前
kilion发布了新的文献求助10
10秒前
李男孩完成签到,获得积分20
10秒前
10秒前
10秒前
10秒前
11秒前
无头骑士完成签到,获得积分10
12秒前
Qz发布了新的文献求助10
13秒前
Hello应助郭娅楠采纳,获得10
14秒前
14秒前
追逐者发布了新的文献求助10
14秒前
赘婿应助专注的故事采纳,获得10
15秒前
16秒前
黄小北发布了新的文献求助10
17秒前
wanci应助无聊的幻露采纳,获得10
19秒前
lifeboast完成签到,获得积分10
19秒前
谢圣林完成签到,获得积分10
19秒前
shelia发布了新的文献求助10
19秒前
19秒前
小二郎应助lifeboast采纳,获得10
21秒前
22秒前
追逐者完成签到,获得积分20
22秒前
23秒前
nk完成签到 ,获得积分10
23秒前
23秒前
24秒前
廿五完成签到 ,获得积分10
24秒前
Qz完成签到,获得积分10
25秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Modern Epidemiology, Fourth Edition 5000
Kinesiophobia : a new view of chronic pain behavior 5000
Molecular Biology of Cancer: Mechanisms, Targets, and Therapeutics 3000
Digital Twins of Advanced Materials Processing 2000
Weaponeering, Fourth Edition – Two Volume SET 2000
Signals, Systems, and Signal Processing 610
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 纳米技术 化学工程 生物化学 物理 计算机科学 内科学 复合材料 催化作用 物理化学 光电子学 电极 冶金 细胞生物学 基因
热门帖子
关注 科研通微信公众号,转发送积分 6018535
求助须知:如何正确求助?哪些是违规求助? 7607517
关于积分的说明 16159358
捐赠科研通 5166108
什么是DOI,文献DOI怎么找? 2765198
邀请新用户注册赠送积分活动 1746765
关于科研通互助平台的介绍 1635364