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

作者：Wang, Qing; Lu, Zhiwei; Zhang, Li; Zhang, Qingqing; Wang, Meiling; Zhao, Huizhen; Liu, Yuehong; Fu, Shuang; Huang, Zhenghai; Xie, Ziye; Yu, Honghong; Zhang, Zhixin; Gao, Xiaoyan^*

来源：Journal of Separation Science, 2017, 40(14): 2854-2862.

DOI：10.1002/jssc.201700203

• chemical characteristic fragments
• fragmentation pathways
• multistage mass spectrometry chromatograms
• postacquisition data mining
• traditional Chinese medicine

Chemical characteristic fragment filtering in MSn 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.

• 出版日期2017-7
• 单位北京中医药大学