Rapid and global detection and characterization of aconitum alkaloids in Yin Chen Si Ni Tang, a traditional Chinese medical formula, by ultra performance liquid chromatography-high resolution mass spectrometry and automated data analysis.
An improved method employing Metabolynx XS with mass defect filter (MDF), a post-acquisition data processing software, was developed and applied for global detection of aconitum alkaloids in Yin Chen Si Ni Tang, a traditional Chinese medical formula (TCMF). The full-scan LC-MS/MS data sets with extra mass were acquired using ultra performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UPLC/Q-TOF-MS) with the MS(E) mode in a single injection. To remove the interferences, Metabolynx XS was optimized to extract the ions of aconitum alkaloids located at the lower abundance. As a result, 62 ions were assigned rapidly to aconitum alkaloids and identified tentatively by comparing the accurate mass and fragments information with that of the authentic standards or by mass spectrometry analysis and retrieving the reference literatures. Compared with the previous studies on Fuzi-containing TCMF, the report detected more aconitum alkaloids, and the analysis process was accelerated by automated data processing. It is concluded that the screening capability of Metabolynx XS with MDF, together with the utilization of MS(E) in structural elucidation, can facilitate a rapid and comprehensive searching and effective structural characterization of aconitum alkaloids in TCMF.
Yan
Guangli
G
Research Institute of Traditional Chinese Medicine, Heilongjiang University of Chinese Medicine, No. 24 Heping Road, Harbin 150040, PR China.
Sun
Hui
H
Sun
Wenjun
W
Zhao
Li
L
Meng
Xiangcai
X
Wang
Xijun
X
D031841:Aconitum / Q000737:chemistry*; D000470:Alkaloids / Q000032:analysis* / Q000737:chemistry*; D002851:Chromatography, High Pressure Liquid / Q000379:methods*; D008516:Medicine, Chinese Traditional; D021241:Spectrometry, Mass, Electrospray Ionization / Q000379:methods*; D013223:Statistics as Topic
D000470:Alkaloids