Valorization of Wild-Type Cannabis indica by Supercritical CO2 Extraction and Insights into the Utilization of Raffinate Biomass
Bibliographic record
Abstract
Supercritical CO2 extraction (SCCO2) extraction of cannabis oil from Indian cannabis (Cannabis indica) leaves was optimized through a central composite design using CO2 pressure (150–250 bar), temperature (30–50 °C) and time (1–2 h). From the regression model, the optimal CO2 pressure, extraction temperature and time were 250 bar, 43 °C and 1.7 h, respectively resulting in the experimental yield of 4.9 wt% of cannabis oil via SCCO2 extraction. The extract contained cannabidiol, tetrahydrocannabivarin, Δ9-tetrahydrocannabinol and Δ8-tetrahydrocannabinol as well as two terpenoids such as cis-caryophyllene and α-humulene. Besides SCCO2 extraction of cannabis oil, the raffinate biomass was utilized to extract polyphenols using water as the extraction medium. Cannabis oil and water extractive were investigated for their half-maximal inhibitory concentration (IC50) values, which were found to be 1.3 and 0.6 mg/mL, respectively. This is comparable to the commercially available antioxidant such as butylated hydroxytoluene with an IC50 value of 0.5 mg/mL. This work on SCCO2 extraction of cannabinoids and other valuable bioactive compounds provides an environmentally sustainable technique to valorize cannabis leaves.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".