Journal:Combined ambient ionization mass spectrometric and chemometric approach for the differentiation of hemp and marijuana varieties of Cannabis sativa
| Full article title | Combined ambient ionization mass spectrometric and chemometric approach for the differentiation of hemp and marijuana varieties of Cannabis sativa |
|---|---|
| Journal | Journal of Cannabis Research |
| Author(s) | Chambers, Megan I.; Beyramysoltan, Samira; Garosi, Benedetta; Musah, Rabi A. |
| Author affiliation(s) | State University of New York |
| Primary contact | Email: rmusah at albany dot edu |
| Year published | 2023 |
| Volume and issue | 5 |
| Article # | 5 |
| DOI | 10.1186/s42238-023-00173-0 |
| ISSN | 2522-5782 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | https://jcannabisresearch.biomedcentral.com/articles/10.1186/s42238-023-00173-0 |
| Download | https://jcannabisresearch.biomedcentral.com/counter/pdf/10.1186/s42238-023-00173-0.pdf (PDF) |
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This article should be considered a work in progress and incomplete. Consider this article incomplete until this notice is removed. |
Abstract
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.
Keywords: Cannabis sativa, ambient ionization mass spectrometry, direct analysis in real time—high-resolution mass spectrometry, multivariate data analysis, random forest, principal component analysis
Background
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Notes
This presentation is faithful to the original, with only a few minor changes to presentation. Some grammar and punctuation was cleaned up to improve readability. In some cases important information was missing from the references, and that information was added. The original lists references in alphabetical order; they are listed by order of appearance for this version, by design.
