Comprehensive classification of USA cannabis samples based on chemical profiles of major cannabinoids and terpenoids

Abstract

Different USA-origin cannabis samples were analyzed by GC-FID to quantify all possible cannabinoids and terpenoids prior to their clustering. Chromatographic analysis confirmed the presence of seven cannabinoids and sixteen terpenoids with variable levels. Among tested cannabinoids, Δ9-Tetrahydrocannabinol Δ9-THC and cannabinol CBN were available in excess amounts (1.2–8.0 wt%) and (0.22–1.1 wt%), respectively. Fenchol was the most abundant terpenoid with a range of (0.03–1.0 wt%). The measured chemical profile was used to cluster 23 USA states and to group plant samples using different unsupervised multivariate statistical tools. Clustering of plant samples and states was sensitive to the selected cannabinoids/terpenoids. Principal component analysis (PCA) indicated the importance of Δ9-THC, CBN, CBG, CBC, THCV, Δ8-THC, CBL, and fenchol for samples clustering. Δ9-THC was significant to separate California-origin samples while CBN and fenchol were dominant to separate Oregon-origin samples away from the rest of cannabis samples. A special PCA analysis was performed on cannabinoids after excluding Δ9-THC (due to its high variability in the same plant) and CBN (as a degradation byproduct for THC). Results indicated that CBL and Δ8-THC were necessary to separate Nevada and Washington samples, while, CBC was necessary to isolate Oregon and Illinois plant samples. PCA based on terpenoids content confirmed the significance of caryophyllene, guaiol, limonene, linalool, and fenchol for clustering target. Fenchol played a major role for clustering plant samples that originated from Washington and Nevada. k-means method was more flexible than PCA and generated three different classes; samples obtained from Oregon and California in comparison to the rest of other samples were obviously separated alone, which attributed to their unique chemical profile. Finally, both PCA and k-means were useful and quick guides for cannabis clustering based on their chemical profile. Thus, less effort, time, and materials will be consumed in addition to decreasing operational conditions for cannabis clustering.