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Analysis of carbazole alkaloids in Murraya koenigii by means of high performance liquid chromatography coupled to Tandem mass spectrometry with a predictive multi experiment approach
- Source :
- Journal of Chromatography Open, Vol 2, Iss , Pp 100055- (2022)
- Publication Year :
- 2022
- Publisher :
- Elsevier, 2022.
-
Abstract
- Murraya koenigii (M. koenigii) is recognized as one of the most significant Indian medicinal plants, due to its therapeutic properties. In fact, various biological activities, including anti-inflammatory and antifungal, are attributed to carbazole alkaloids (CAs) and their metabolites, but it is still difficult to achieve a full classification of these compounds because of their heterogeneous structures. For this reason, the development of new strategies for their identification is necessary. In this work, a reliable method, including the simultaneous quantification of three main CAs of Murraya (Mahanimbine, Koenimbine and Koenigicine) and a putative identification for other compounds belonging to the huge family of CAs, was developed by means of HPLC-MS/MS. Also, an efficient extraction procedure followed by a suitable clean-up step was presented, in order to obtain reliable recoveries (resulted from 60 to 85% for all the analytes). The analyses were performed by using predictive multi experiment approach based on information-dependent acquisition (IDA), coupling multiple reaction monitoring (MRM) and precursor ion (PI) as survey scans, and enhanced product ion scan (EPI) as dependent scan. Competitive Fragmentation Modeling-ID (CFM-ID) was used to predict MS/MS spectra from the chemical structures of the compounds in order to create a suitable MRM inclusion list. The obtained results showed that this method can simultaneously provide quantitative information for the target analytes (Mahanimbine (7.22–5.62 mg/kg), Koenimbine (1.26–1.62 mg/kg) and Koenigicine (0.44–1.77 mg/kg)) and a putative identification for several compounds belonging to different classes of CAs which are not included in the target list, thanks to PI survey scan.
Details
- Language :
- English
- ISSN :
- 27723917
- Volume :
- 2
- Issue :
- 100055-
- Database :
- Directory of Open Access Journals
- Journal :
- Journal of Chromatography Open
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.29f98501fe414c61acea6f3209be44f3
- Document Type :
- article
- Full Text :
- https://doi.org/10.1016/j.jcoa.2022.100055