RESUMO
Traditional methods for detecting and quantifying cannabinoids in Cannabis sativa materials are most often chromatography-based, and they generally require extensive sample preparation protocols to render materials into a form that can be injected into the systems without the risk of contaminating or damaging the equipment. This challenge is amplified when interrogating the increasingly broad range of matrix types that cannabinoids are infused within, such as edibles that also contain sugars, fats, lipids, and carbohydrates. The requisite application of highly nuanced methods that must be developed for each matrix type is, in addition to being resource-intensive and time-consuming, highly impractical and unsustainable for crime laboratories endeavoring to perform such analyses in a routine manner, since they are often under-resourced while typically also confronting sample testing backlogs. A key to resolving this issue is to identify an analysis approach that avoids the requirement for nuanced method development by being applicable to a broader range of matrix types. Ambient ionization mass spectrometry (AIMS) methods have shown great promise in their ability to rapidly interrogate samples. Therefore, this study focused on developing validated protocols using AIMS (specifically, direct analysis in real time-high-resolution mass spectrometry, or DART-HRMS) to detect and quantify Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD) in edible matrices. Calibration curves were developed using deuterated counterparts of THC and CBD as internal standards. Following the use of high cannabinoid recovery rate extraction protocols for chocolates and gelatin-based fruit candies or "gummies", the DART-HRMS approach was applied to quantify cannabinoid levels in commercially available cannabinoid-infused candies, yielding results similar to those reported on the product labels. Importantly, the developed method circumvented challenges encountered using traditional approaches. As the Cannabis field continues to evolve and new matrix types emerge on the market, the DART-HRMS detection and quantification protocols can be readily applied without the need for major procedural adaptations.
RESUMO
BACKGROUND: Hemp and marijuana are the two major varieties of Cannabis sativa. While both contain Δ9-tetrahydrocannabinol (THC), the primary psychoactive component of C. sativa, they differ in the amount of THC that they contain. Presently, U.S. federal laws stipulate that C. sativa containing greater than 0.3% THC is classified as marijuana, while plant material that contains less than or equal to 0.3% THC is hemp. Current methods to determine THC content are chromatography-based, which requires extensive sample preparation to render the materials into extracts suitable for sample injection, for complete separation and differentiation of THC from all other analytes present. This can create problems for forensic laboratories due to the increased workload associated with the need to analyze and quantify THC in all C. sativa materials. METHOD: The work presented herein combines direct analysis in real time-high-resolution mass spectrometry (DART-HRMS) and advanced chemometrics to differentiate hemp and marijuana plant materials. Samples were obtained from several sources (e.g., commercial vendors, DEA-registered suppliers, and the recreational Cannabis market). DART-HRMS enabled the interrogation of plant materials with no sample pretreatment. Advanced multivariate data analysis approaches, including random forest and principal component analysis (PCA), were used to optimally differentiate these two varieties with a high level of accuracy. RESULTS: When PCA was applied to the hemp and marijuana data, distinct clustering that enabled their differentiation was observed. Furthermore, within the marijuana class, subclusters between recreational and DEA-supplied marijuana samples were observed. A separate investigation using the silhouette width index to determine the optimal number of clusters for the marijuana and hemp data revealed this number to be two. Internal validation of the model using random forest demonstrated an accuracy of 98%, while external validation samples were classified with 100% accuracy. DISCUSSION: The results show that the developed approach would significantly aid in the analysis and differentiation of C. sativa plant materials prior to launching painstaking confirmatory testing using chromatography. However, to maintain and/or enhance the accuracy of the prediction model and keep it from becoming outdated, it will be necessary to continue to expand it to include mass spectral data representative of emerging hemp and marijuana strains/cultivars.