MATLAB language assisted data acquisition and processing in liquid chromatography Orbitrap mass spectrometry: Application to the identification and differentiation of Radix Bupleuri from its adulterants.

Comprehensive and rapid analysis of secondary metabolites like saponins remains challenging. This study aimed to establish a semi-automated workflow for filtration, identification, and characterization of saikosaponins in six Bupleurum species. Radix Bupleuri, a high-sales herbal medicine, is often adulterated, restricting its quality control and applications. Two authentic Radix Bupleuri species and four major adulterants were analyzed through UHPLC-LTQ-Orbitrap-MS for targeted saikosaponin analysis. To reveal trace saikosaponins and obtain quality fragment data, a MATLAB-based process automatically enumerating "sugar chain + aglycone + side chain" combinations and deduplicating generated a predicted saikosaponin database covering all possible saikosaponins as a precursor ion list for comprehensive targeted acquisition. To focus on informative ions and reduce MS analysis workload, we utilized MATLAB to automatically filtrate the false positive ions by MS ¹ and MS ² spectrometry. The newly established MATLAB-assisted data acquisition approach exhibited 50 % improvement in characterization of targeted saikosaponins. Furthermore, positive and negative ionization workflows were designed for accurate saikosaponins characterization based on fragmentation rules. In total, 707 saikosaponins were characterized, including over 500 potential new compounds and previously unreported C29 aglycones. We identified 25 saikosaponins present in both authentic species but absent in adulterants as potential markers. This unprecedented comprehensive multi-origin species differentiation demonstrates the promise of MATLAB-assisted acquisition and processing to advance saponin identification and standardize the Radix Bupleuri market.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023. Published by Elsevier B.V.)