Molecular Systems, Organic and Biological Analytical Chemistry Group, University of Liège, 11 Allée du Six Août, 4000 Liège, Belgium.
Anal Chem. 2020 Aug 4;92(15):10512-10520. doi: 10.1021/acs.analchem.0c01301. Epub 2020 Jul 13.
The present research reports on the development of a methodology to unravel the complex phytochemistry of cannabis. Specifically, cannabis inflorescences were considered and stir bar sorptive extraction (SBSE) was used for the preconcentration of the metabolites. Analytes were thermally desorbed into a comprehensive two-dimensional (2D) gas chromatography (GC × GC) system coupled with low- and high-resolution mass spectrometry (MS). Particular attention was devoted to the optimization of the extraction conditions, to extend the analytes' coverage, and the chromatographic separation, to obtain a robust data set for further untargeted analysis. Monoterpenes, sesquiterpenes, hydrocarbons, cannabinoids, other terpenoids, and fatty acids were considered to optimize the extraction conditions. The response of selected ions for each chemical class, delimited in specific 2D chromatographic regions, enabled an accurate and fast evaluation of the extraction variables (i.e., time, temperature, solvent, salt addition), which were then selected to have a wide analyte selection and good reproducibility. Under optimized SBSE conditions, eight different cannabis inflorescences and a quality control sample were analyzed and processed following an untargeted and unsupervised approach. Principal component analysis on all detected metabolites revealed chemical differences among the sample types which could be associated with the plant subspecies. With the same SBSE-GC × GC-MS methodology, a quantitative targeted analysis was performed on three common cannabinoids, namely, Δ9-tetrahydrocannabinol, cannabidiol, and cannabinol. The method was validated, giving correlation factors over 0.98 and <20% reproducibility (relative standard deviation). The high-resolution MS acquisition allowed for high-confidence identification and post-targeted analysis, confirming the presence of two pesticides, a plasticizer, and a cannabidiol degradation product in some of the samples.
本研究报告开发了一种方法来揭示大麻的复杂植物化学。具体来说，考虑了大麻花序，并使用搅拌棒吸附萃取（SBSE）对代谢物进行预浓缩。分析物被热解吸到二维（2D）气相色谱（GC×GC）系统中，并与低分辨率和高分辨率质谱（MS）联用。特别关注优化提取条件，以扩大分析物的覆盖范围和色谱分离，以获得用于进一步非靶向分析的稳健数据集。考虑了单萜、倍半萜、烃类、大麻素、其他萜类和脂肪酸来优化提取条件。每个化学类别的选定离子的响应，限定在特定的 2D 色谱区域内，使我们能够准确快速地评估提取变量（即时间、温度、溶剂、加盐），然后选择这些变量来获得广泛的分析物选择和良好的重现性。在优化的 SBSE 条件下，分析并处理了八种不同的大麻花序和一个质量控制样品，采用非靶向和无监督的方法。对所有检测到的代谢物进行主成分分析，揭示了样品类型之间的化学差异，这些差异可能与植物亚种有关。使用相同的 SBSE-GC×GC-MS 方法，对三种常见大麻素（即 Δ9-四氢大麻酚、大麻二酚和大麻醇）进行了定量靶向分析。该方法得到了验证，相关系数均大于 0.98，重现性（相对标准偏差）小于 20%。高分辨率 MS 采集允许进行高置信度鉴定和靶向后分析，确认在一些样品中存在两种农药、一种增塑剂和一种大麻二酚降解产物。
