Applying target data screening followed by characteristic fragment filtering for the comprehensive screening and identification of alkaloids in: Corydalis yanhusuo W. T. Wang by UPLC-Q-TOF/MSE

Abstract

A target data screening strategy followed by characteristic fragment filtering by UPLC-Q-TOF/MSE was developed for rapidly and comprehensively identifying alkaloids in Corydalis yanhusuo W. T. Wang (Yanhusuo). The proposed strategy consisted of the following four steps. (1) Fragmental patterns and characteristic fragments of various types of alkaloids were summarized based on the reference compounds and previous reference literature. (2) Target data screening was conducted and data screening tables involving various types of alkaloids were constructed referring to the structural characteristics of alkaloids, the type and number of substituents, and characteristic fragments. This data screening table includes all possible molecular weights of various types of alkaloids. (3) The raw data detected by UPLC-Q-TOF/MSE were screened preliminarily using data screening tables, then characteristic fragment filtering was used to rapidly recognize various types of alkaloids. (4) A comparison was made with retention time, accurate mass, MS/MS fragmentation and online databases, as well as a reference to related literature to validate the data. As a result, a total of 86 compounds were identified or characterized, including 24 tetrahydroprotoberberine alkaloids, 22 protoberberine alkaloids, 6 protopine alkaloids, 12 aporphine alkaloids and 22 other compounds. Among them, 8 were potentially new compounds, and 5 components were discovered in Yanhusuo for the first time. The method proposed in this paper was proved to be an efficient data processing approach to rapidly discover and characterize chemical constituents from complicated herbal extracts, without the help of standard substances. Furthermore, this research enriched the material basis of Yanhusuo and provided meaningful guidance for the discovery of potentially new compounds.