Method development for an untargeted HS-SPME-GC-MS analysis of terpenes and cannabinoids for the geographical sourcing of Marijuana

Despite growing decriminalization of Cannabis sativa (i.e., marijuana) possession throughout the United States of America, there remains to be an ongoing interest in the detection of unlawfully possessed and transported marijuana. This issue has resulted in an increasing interest regarding the generalization and specification related to the canine detection of marijuana. More specifically, canine trainers have expressed concerns on whether canines can generalize on the odor of marijuana regardless of the origin of their training materials. This research aims to differentiate multiple marijuana headspace samples from three regions in the USA based solely on the volatile organic compounds (VOCs) found in their odor profiles. In this study, a heated headspace solid phase micro-extraction (SPME) technique was optimized and implemented for the collection of both volatile terpenes and cannabinoids from marijuana. The headspace samples were analyzed using two full-scan, untargeted, optimized methods on a gas chromatograph coupled to a mass spectrometer (GC-MS), and a variety of chemometric tools were applied to the data to enable differentiation and potential classification between sample populations. Principal component analysis and sparse partial least squares discriminant analysis (sPLS-DA) employed in this study have demonstrated a disparity between marijuana varieties based on geography using the VOCs extracted from their odor profiles. With this research, it is intended to determine some fundamental differences between Cannabis of different geographical origins and set a foundation for the development and advancement of instrumental applications for other non-contact marijuana detection techniques in support of the improvement of illicit substance detection technology.