Cannabinoids are a group of compounds that bind to cannabinoid receptors. They possess pharmacological properties like that of the plant Cannabis sativa. Gas chromatography (GC) is one of the popular chromatographic techniques that has been routinely used in the analysis of cannabinoids in different matrices. The article aims to review the literature on the application of GC-based analytical methods for the analysis of phytocannabinoids published during the period from January 2020 to August 2023. A thorough literature search was conducted using different databases, like Web of Knowledge, PubMed, 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. From the search results, only the publications that incorporate the GC analysis of phytocannabinoids were reviewed, and papers on synthetic cannabinoids were excluded. Since the publication of the review article on GC analysis of phytocannabinoids in early 2020, several GC-based methods for the analysis of phytocannabinoids have appeared in the literature. While simple 1D GC-mass spectrometry (MS) and GC-flame ionisation detector (FID) methods are still quite common in phytocannabinoids analysis, 2D GC-MS and GC-MS/MS are increasingly becoming popular, as these techniques offer more useful data for identification and quantification of phytocannabinoids in various matrices. The use of automation in sample preparation and the utilisation of mathematical and computational models for optimisation of different protocols have become a norm in phytocannabinoids analysis. Pre-analyses have been found to incorporate different derivatisation techniques and environmentally friendly extraction protocols. GC-based analysis of phytocannabinoids, especially using GC-MS, remains one of the most preferred methods for the analysis of these compounds. New derivatisation methods, ionisation techniques, mathematical models, and computational approaches for method optimisation have been introduced.
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