Laboratory of Growth Regulators, Palacký University and Institute of Experimental Botany, The Czech Academy of Sciences, Olomouc, Czech Republic.
Department of Bioinformatics, National Institute of Research and Development for Biological Sciences, Bucharest, Romania.
Phytochem Anal. 2023 Dec;34(8):903-924. doi: 10.1002/pca.3303. Epub 2023 Nov 14.
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.
大麻素是一组与大麻素受体结合的化合物。它们具有与大麻植物相似的药理学特性。气相色谱(GC)是一种常用的色谱技术,已常规用于分析不同基质中的大麻素。
本文旨在综述 2020 年 1 月至 2023 年 8 月期间发表的基于 GC 的分析方法在植物大麻素分析中的应用文献。
使用不同的数据库,如 Web of Knowledge、PubMed、Google Scholar 和其他相关已发表材料(包括已发表的书籍),进行了全面的文献检索。在搜索中,使用了不同的关键词组合,大麻素在所有组合中都存在,关键词是大麻素、大麻、大麻、分析、GC、定量、定性和质量控制。从搜索结果中,仅对包含植物大麻素 GC 分析的出版物进行了综述,排除了合成大麻素的论文。
自 2020 年初发表关于 GC 分析植物大麻素的综述文章以来,文献中出现了几种基于 GC 的植物大麻素分析方法。虽然简单的 1D GC-质谱(MS)和 GC-火焰离子化检测器(FID)方法在植物大麻素分析中仍然相当常见,但 2D GC-MS 和 GC-MS/MS 越来越受欢迎,因为这些技术为各种基质中植物大麻素的鉴定和定量提供了更有用的数据。在样品制备中使用自动化以及为优化不同方案而使用数学和计算模型已成为植物大麻素分析的常态。预分析已被发现采用了不同的衍生化技术和环保提取方案。
基于 GC 的植物大麻素分析,特别是使用 GC-MS,仍然是分析这些化合物的最受欢迎方法之一。已经引入了新的衍生化方法、离子化技术、数学模型和计算方法来优化方法。