4.8 Article

In-Depth Cannabis Multiclass Metabolite Profiling Using Sorptive Extraction and Multidimensional Gas Chromatography with Low- and High-Resolution Mass Spectrometry

Journal

ANALYTICAL CHEMISTRY
Volume 92, Issue 15, Pages 10512-10520

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acs.analchem.0c01301

Keywords

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Funding

  1. FWO/FNRS Belgium EOS Grant [30897864]
  2. Division of Analytical Chemistry of the European Chemical Society

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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 x 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 x GC-MS methodology, a quantitative targeted analysis was performed on three common cannabinoids, namely, Delta 9-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 posttargeted analysis, confirming the presence of two pesticides, a plasticizer, and a cannabidiol degradation product in some of the samples.

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