Comprehensive Profiling of Terpenes and Terpenoids in Different Cannabis Strains Using GC × GC-TOFMS
Bibliographic record
Abstract
Cannabis contains a wide range of terpenes and terpenoids that are mainly responsible for their distinctive aroma and flavor. These compounds have also demonstrated therapeutic effects either alone and/or as synergistic compounds with other terpenes, terpenoids, and/or cannabinoids. Several studies have attempted to fully characterize terpenes and terpenoids in cannabis; however, most of these studies used one-dimensional gas chromatography, which often results in the co-elution of the compounds. In the present study, we analyzed terpenes and terpenoids in the dried flowers of six cannabis strains using a two-dimensional gas chromatograph time-of-flight mass spectrometer (GC × GC-TOFMS). A total of 146 terpenes and terpenoids were detected across all six cannabis strains with an enhanced separation of 16 terpenes and terpenoids in the second dimension. Additionally, we achieved enhanced separation of four terpenes and terpenoids from a standard mixture in the second dimension. Chemical differences were observed in the number and relative abundance of monoterpenes, monoterpenoids, sesquiterpenes, and sesquiterpenoids in all six strains. We were also able to identify four new terpenoids in cannabis, which are reported here for the first time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".