Chromatographic characterisation of 11 phytocannabinoids: Quantitative and fit‐to‐purpose performance as a function of extra‐column variance
Bibliographic record
Abstract
Abstract Introduction Cannabis sativa L. (cannabis) is utilised as a therapeutic and recreational drug. With the legalisation of cannabis in many countries and the anticipated regulation of potency that will accompany legalisation, analytical testing facilities will require a broadly applicable, quantitative, high throughput method to meet increased demand. Current analytical methods for the biologically active components of cannabis (phytocannabinoids) suffer from low throughput and/or an incomplete complement of relevant phytocannabinoids. Objective To develop a rapid, quantitative and broadly applicable liquid chromatography–tandem mass spectrometry analytical method for 11 phytocannabinoids in cannabis with acidic and neutral character. Methodology Bulk diffusion coefficients were calculated using the Taylor–Aris open tubular method, with four reference compounds used to validate the experimental set‐up. Three columns were quantitatively evaluated using van Deemter plots and fit‐to‐purpose performance metrics. Low (1.2 μL2) and standard (3.6 μL2) extra‐column variance ultra‐high pressure liquid chromatography (UPLC) configurations were contrasted. Method performance was demonstrated with methanolic cannabis flower extracts. Results Bulk diffusion coefficients and van Deemter plots for 11 phytocannabinoids are reported. The developed chromatographic method includes the challenging Δ8/Δ9‐tetrahydrocannabinol isobars and, at 6.5 min, is faster than existing methods targeting similar panels of biologically active phytocannabinoids. Conclusions The bulk diffusion coefficients and van Deemter curves informed the development of a rapid quantitative method and will facilitate potential expansion to include additional compounds, including synthetic cannabinoids. The developed method can be implemented with low or standard extra‐column variance UPLC configurations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".