Gas chromatographic analysis of naturally occurring cannabinoids: A review of literature published during the past decade

0531071 - ÚEB 2021 RIV GB eng J - Journal Article
Nahar, Lutfun - Guo, M. - Sarker, S. D.
Gas chromatographic analysis of naturally occurring cannabinoids: A review of literature published during the past decade.
Phytochemical Analysis. Roč. 31, č. 2 (2020), s. 135-146. ISSN 0958-0344. E-ISSN 1099-1565
Institutional support: RVO:61389030
Keywords : analysis * cannabinoids * cannabis * Cannabis sativa * detection * gas chromatography (GC) * gc-fid * gc–ms * marijuana
OECD category: Analytical chemistry
Impact factor: 3.373, year: 2020
Method of publishing: Open access
http://doi.org/10.1002/pca.2886

Introduction: Cannabinoids are organic compounds, natural or synthetic, that bind to the cannabinoid receptors and have similar pharmacological properties as produced by the cannabis plant, Cannabis sativa. Gas chromatography (GC), e.g. gas chromatography mass spectrometry (GC–MS), is a popular analytical tool that has been used extensively to analyse cannabinoids in various matrices. Objective: To review published literature on the use of various GC-based analytical methods for the analysis of naturally occurring cannabinoids published during the past decade. Methodology: A comprehensive literature search was performed utilising several databases, like Web of Knowledge, PubMed and Google Scholar, and other relevant published materials including published books. The keywords used, in various combinations, with cannabinoids being present in all combinations, in the search were cannabinoids, Cannabis sativa, marijuana, analysis, GC, quantitative, qualitative and quality control. Results: During the past decade, several GC-based methods for the analysis of cannabinoids have been reported. While simple one-dimensional (1D) GC–MS and GC-FID (flame ionisation detector) methods were found to be quite common in cannabinoids analysis, two-dimensional (2D) GC–MS as well as GC–MS/MS also were popular because of their ability to provide more useful data for identification and quantification of cannabinoids in various matrices. Some degree of automation in sample preparation, and applications of mathematical and computational models for optimisation of different protocols were observed, and pre-analyses included various derivatisation techniques, and environmentally friendly extraction protocols. Conclusions: GC-based analysis of naturally occurring cannabinoids, especially using GC–MS, has dominated the cannabinoids analysis in the last decade, new derivatisation methods, new ionisation methods, and mathematical models for method optimisation have been introduced.
Permanent Link: http://hdl.handle.net/11104/0309821