Profiling of various dry Cannabis sativa from Aceh, Indonesia, based on cannabinoids spectroscopy characteristics
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Background
Cannabis, which is a psychoactive plant, refers to the leaves, flowers, stems, and seeds. Cannabis is used globally for its psychoactive properties, with 2.5% of the world's population consuming it for recreational purposes. But in Indonesia, the plant is classified as a Class 1 narcotic, with a prevalence of use reaching 41.4%. Aceh is one of the largest cannabis-producing regions in Indonesia due to its favorable geographical and climatic conditions. Despite its illegal status, cannabis contains valuable phytocannabinoid compounds and is potentially important in medical applications. Previous studies have shown a correlation between the compound profile of cannabis and its geographical origin. This study aims to develop a classification method based on the cannabinoids compound profiles of dried cannabis samples taken from five regions in Aceh (Aceh Besar, Aceh Tengah, Bireuen, Lhokseumawe, and Pidie Jaya) by microscopy, Raman spectrophotometry, GC–MS, and parametric statistical analysis to assist authorities in tracing the source of cannabis for law enforcement and forensic purposes.
Results
In this study, dried Cannabis sativa from five regions of Aceh, Indonesia, was tested with Raman spectroscopy and GC–MS to produce informative cannabinoid compound profiles as plant profiling. The results obtained 10 cannabinoids quantified in plant samples (Δ9-THC, CBD, THCV, CBL, CBTC, Methoxy-THC, CBC, CBG, Δ9-THCH, and CBN). The cannabinoids compound profile showed Δ9-THC had the highest overall content and was indicated as the most important compound in the cannabis plant clustering profile. Among the various regions, Aceh Besar had the highest cannabis content. Statistical analysis of Raman spectroscopy and GC–MS data found (1) revealed compounds responsible for clustering cultivars between clusters, (2) variation among cannabis chemical profiles as a result of growing environment, and (3) facilitated prediction of cannabis profiles in helping to categorise regions of unknown cannabis origin based on chemical profiles.
Conclusion
Raman spectroscopy and GC–MS have proven reliable and efficient methods for classifying Cannabis sativa based on its cannabinoid profile in Aceh, Indonesia. The findings help reveal the geographical origin of the growing location of cannabis plant specimens. All five cannabis samples contained a major Δ9-THC psychoactive constituent. The highest Δ9-THC content comes from AB due to the influence of environmental factors. Parametric test analysis concluded that there was no significant effect of geographical origin related to the relatively close distance range of samples. Additionally, comparing these methods with other analytical techniques will support defined classification models and improve their application in forensic science, particularly in drug enforcement and quality assessment.
