化学
吲哚试验
光栅图形
串联质谱法
轨道轨道
质谱法
色谱法
组合化学
立体化学
计算机科学
人工智能
作者
Huiqin Pan,Changliang Yao,Wenzhi Yang,Yao Shuai,Yong Huang,Yibei Zhang,Wanying Wu,De‐An Guo
标识
DOI:10.1016/j.chroma.2018.05.066
摘要
Comprehensive chemical profiling is of great significance for understanding the therapeutic material basis and quality control of herbal medicines, which is challenging due to its inherent chemical diversity and complexity, as well as wide concentration range. In this study, we introduced an enhanced strategy integrating offline two-dimensional (2D) separation and the step-wise precursor ion list-based raster-mass defect filter (step-wise PIL-based raster-MDF) scan by tandem LTQ-Orbitrap mass spectrometer. A comprehensive analysis of indole alkaloids in five botanical origins of Uncariae Ramulus Cum Unicis (Gou-Teng) was used as an exemplary application. A positively charged reversed phase (PR) × conventional RP LC system in different pH conditions was constructed with the orthogonality of 74%. A theoretical step-wise PIL among 310–950 Da with the step-size of 2 Da was developed to selectively trigger fragmentations and extend the coverage of potential indole alkaloids. Simultaneously, by defining parent mass width (PMW) of the step-wise PIL to ±55 mDa, a raster-MDF screening was achieved in the acquisition process. Additionally, subtype classification and structural elucidation were facilitated by a four-step interpretation strategy. As a result, a total of 1227 indole alkaloids were efficiently exposed and characterized from five botanical origins of Gou-Teng, which showed high chemical diversity. A systematic comparison among five species was first performed and only 66 indole alkaloids were common. For method validation, three new alkaloid N-oxides were isolated and unambiguously identified by NMR. The present study provides a novel data-dependent acquisition method with improved target coverage and high selectivity. The integrated strategy is practical to efficiently expose and comprehensively characterize complex components in herbal medicines.
科研通智能强力驱动
Strongly Powered by AbleSci AI