Identification of Chemotypic Markers in Three Chemotype Categories of Cannabis Using Secondary Metabolites Profiled in Inflorescences, Leaves, Stem Bark, and Roots
Bibliographic record
Abstract
Previous chemotaxonomic studies of cannabis only focused on tetrahydrocannabinol (THC) dominant strains while excluded the cannabidiol (CBD) dominant strains and intermediate strains (THC ≈ CBD). This study investigated the utility of the full spectrum of secondary metabolites in different plant parts in three cannabis chemotypes (THC dominant, intermediate, and CBD dominant) for chemotaxonomic discrimination. Hierarchical clustering, principal component analysis (PCA), and canonical correlation analysis assigned 21 cannabis varieties into three chemotypes using the content and ratio of cannabinoids, terpenoids, flavonoids, sterols, and triterpenoids across inflorescences, leaves, stem bark, and roots. The same clustering results were obtained using secondary metabolites, omitting THC and CBD. Significant chemical differences were identified in these three chemotypes. Cannabinoids, terpenoids, flavonoids had differentiation power while sterols and triterpenoids had none. CBD dominant strains had higher amounts of total CBD, cannabidivarin (CBDV), cannabichromene (CBC), α-pinene, β-myrcene, (-)-guaiol, β-eudesmol, α-eudesmol, α-bisabolol, orientin, vitexin, and isovitexin, while THC dominant strains had higher total THC, total tetrahydrocannabivarin (THCV), total cannabigerol (CBG), camphene, limonene, ocimene, sabinene hydrate, terpinolene, linalool, fenchol, α-terpineol, β-caryophyllene, trans-β-farnesene, α-humulene, trans-nerolidol, quercetin, and kaempferol. Compound levels in intermediate strains were generally equal to or in between those in CBD dominant and THC dominant strains. Overall, with higher amounts of β-myrcene, (-)-guaiol, β-eudesmol, α-eudesmol, and α-bisabolol, intermediate strains more resemble CBD dominant strains than THC dominant strains. The results of this study provide a comprehensive profile of bioactive compounds in three chemotypes for medical purposes. The simultaneous presence of a predominant number of identified chemotype markers (with or without THC and CBD) could be used as chemical fingerprints for quality standardization or strain identification for research, clinical studies, and cannabis product manufacturing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".