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应用特征碎片过滤技术,通过高效液相色谱-线性离子阱-Orbitrap 质谱联用技术快速检测和鉴定大黄中的成分。

Applying characteristic fragment filtering for rapid detection and identification of ingredients in rhubarb by HPLC coupled with linear ion trap-Orbitrap mass spectrometry.

机构信息

School of Chinese Materia Medicine, Beijing University of Chinese Medicine, Beijing, China.

出版信息

J Sep Sci. 2017 Jul;40(14):2854-2862. doi: 10.1002/jssc.201700203. Epub 2017 Jun 12.

Abstract

Chemical characteristic fragment filtering in MS chromatograms was proposed to detect and identify the components in rhubarb rapidly using high-performance liquid chromatography coupled with linear ion trap-Orbitrap mass spectrometry. Characteristic fragments consist of diagnostic ions and neutral loss fragments. Characteristic fragment filtering is a postacquisition data mining method for the targeted screening of groups with specific structures, including three steps: first, in order to comprehensively summarize characteristic fragments for global identification of the ingredients in rhubarb, representative authentic standards of dominant chemical categories contained in rhubarb were chosen, from which fragmentation rules and a characteristic fragments schedule were proposed; second, characteristic fragment filtering was used to rapidly recognize analogous skeletons; finally, combined with retention time, accurate mass, characteristic fragments, and previous literature, the structures of the filtered compounds were identified or tentatively characterized. As a result, a total of 271 compounds were detected and identified in rhubarb, including 34 anthraquinones, 83 anthrones, 46 tannins, 17 stilbenes, 24 phenylbutanones, 26 acylglucosides, 26 chromones, and 15 other compounds, 69 of which are potentially new compounds. The proposed characteristic fragment filtering strategy would be a reference for the large-scale detection and identification of the ingredients of herbal medicines.

摘要

采用高效液相色谱-线性离子阱/轨道阱质谱联用技术,提出了 MS 色谱中的化学特征碎片过滤法,用于快速检测和鉴定大黄中的成分。特征碎片由诊断离子和中性丢失碎片组成。特征碎片过滤是一种用于靶向筛选具有特定结构的化合物的采集后数据挖掘方法,包括三个步骤:首先,为了全面总结大黄中成分的特征碎片,用于大黄中所含主要化学类别的代表性真实标准品被选择,从中提出了碎片规则和特征碎片表;其次,利用特征碎片过滤法快速识别类似的骨架;最后,结合保留时间、精确质量、特征碎片和先前的文献,对筛选出的化合物的结构进行鉴定或初步表征。结果,在大黄中共检测到并鉴定了 271 种化合物,包括 34 种蒽醌类、83 种蒽酮类、46 种鞣质类、17 种二苯乙烯类、24 种苯丁酮类、26 种酰基葡萄糖苷类、26 种色酮类和 15 种其他化合物,其中 69 种可能是新化合物。所提出的特征碎片过滤策略将为中草药成分的大规模检测和鉴定提供参考。

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