Supercritical Fluid Extraction of Bioactive Components from St. John's Wort (<i>Hypericum perforatum</i> L.) and <i>Ginkgo biloba</i>
Bibliographic record
Abstract
Supercritical fluid extraction (SFE) offers an attractive alternative to solvent-based methods for extraction and manufacturing of herbal products. In addition to being a "green" solvent, supercritical CO2 can also be selective for extraction and separation of active ingredients from certain herbs. The main active constituents in St. John's wort are hyperforin and adhyperforin, which can be effectively extracted with neat CO2 under mild conditions (30 °C and 80 atm). Furthermore, extraction of hyperforin and adhyperforin from St. John's wort with neat CO2 is selective, resulting in a fairly enriched and stable extract of these compounds. A successful supercritical CO2 extraction of terpene trilactones (bilobalide and ginkgolides A, B and C) from Ginkgo biloba leaves can be achieved at an elevated temperature (100 °C) and high pressure (350 atm; 0.72 g/ml density) with a small amount of ethanol/acetic acid (9:1) as a modifier. SFE of bilobalide and ginkgolides under these conditions resulted in an approximately 10% higher yield compared with traditional solvent based extractions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".