Meta‐analysis and review of cannabinoids extraction and purification techniques
Bibliographic record
Abstract
Abstract Projected revenues of cannabis concentrates and extracts in Canada will reach 5 billion dollars, of which infused products will account for half of the total. The pharmacologically active cannabinoids accumulate in the crop's flowers, accounting for as much as 30% of their dry mass, and are absent from the rest of the plant's body. To achieve a cost effective drug formulation requires optimizing cannabis processing techniques. Here, we review the pre‐treatment of Cannabis sativa L. , its solvent extraction, and the isolation of its active metabolites. We describe traditional extraction processes such as maceration and percolation with organic solvents, but focus on recent green solvent and methods including supercritical fluid extraction (SCFE) and microwave‐ and ultrasound‐enhanced techniques. Furthermore, we report the decarboxylation kinetics to convert tetrahydrocannabinolic acid and cannabidiolic acid and purification‐isolation techniques to satisfy regulatory and consumer requirements. Cannabinoids decarboxylate in 10–60 min at 100–150°C. Ethanol and petroleum ether recover up to 90% of the neutral cannabinoids from plant inflorescences, but the crude extracts require further refining as the purity is less than 50%. Propane and butane compressed gas extraction facilitate solvent removal but introduce safety hazards related to flammability. SCFE is the safest solvent‐free extraction method with improved terpenoid recovery and purity. Academic and commercial interest in the field is expected to accelerate in the next decade due to recent changes in regulatory schemes across North America, which will reduce legal and stigmatic barriers to research.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.021 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".