Fungal Contamination Monitoring in Legal Cannabis Using Ultra-High Performance Liquid Chromatography Tandem Mass Spectrometry
Bibliographic record
Abstract
ABSTRACT Health Canada’s mission to help the people of Canada maintain and improve their health requires rigorous testing of cannabis legal products. The contaminants of cannabis products need to meet the requirements set out in the Cannabis Act and Regulations. Mycotoxins are known cannabis contaminants. They are secondary metabolites produced by fungi that, when exposed to humans, can cause serious health issues. Numerous fungal species have been detected on cannabis, notably, the toxigenic Aspergillus spp, Penicillium spp, and Fusarium spp. These fungal species produce aflatoxins, ochratoxin A, and deoxynivalenol, respectively. To ensure product safety for Canadian consumers, a detection and quantification ultra-high performance liquid chromatography tandem mass spectrometry method was devised for these mycotoxins. Contrary to other published methods, the present method does not require either costly immunoaffinity columns or isotope-labeled internal standards. The method was validated on multiple samples of cannabis plants. It was shown that accurate quantification in plant samples requires a standard addition curve. Limits of detection and quantification were sufficient for regulatory and monitoring purposes. This method could decrease compliance-related costs for the legal cannabis industry.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".