Determination of Cannabinoids in <i>Cannabis sativa</i> Dried Flowers and Oils by LC-UV: Single-Laboratory Validation, First Action 2018.10
Bibliographic record
Abstract
BACKGROUND: Legalization of Cannabis across many U.S. states and in Canada had led to an urgent need for validated analytical methods for the quantitation of cannabinoids in Cannabis sativa L. flowers and finished products. The AOAC Stakeholder Panel on Strategic Food Analytical Methods Cannabis Expert Review Panel (ERP) approved an HPLC-diode-array detection (DAD) method for First Action Official MethodsSM status. OBJECTIVE: To present Official Methods of AnalysisSM (OMA) 2018.10 method details, validation results, and additional method extension data as approved by the ERP and further requirements for Final Action Official MethodsSM status. METHODS: This previously published method used 80% aqueous methanol via sonication for extracting eight cannabinoids-tetrahydrocannabidiolic acid, tetrahydrocannabinol, cannabidiolic acid, cannabidiol, tetrahydrocannabivarin, cannabigerol, cannabinol, and cannabichromene-in dried flowers followed by reversed-phase chromatographic separation and UV detection. RESULTS: The original method underwent extensive method optimization and a single-laboratory validation. Additional requirements requested by the Standard Method Performance Requirement (SMPR®) included a method extension, which was performed to collect repeatability data on two additional cannabinoids: cannabidivarinic acid and cannabigerolic acid. The methods performance was compared with the AOAC SMPR 2017.002 and 2017.001. RSDr ranged from 0.78 to 10.08% and recoveries from 90.7 to 99.2% in several different chemotypes. CONCLUSIONS: The ERP adopted the method and provided recommendations for achieving Final Action status. HIGHLIGHTS: After submission of additional validation data, an HPLC-DAD method for quantitation of cannabinoids in dried flowers and oils was accepted for First Action Official Method status (OMA 2018.10).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".