Supercritical carbon dioxide extraction and fractionation of hyper for in and adhyperforin from St. John's Wort (Hypericum perforatum L.)
Bibliographic record
Abstract
Plants of the genus Hypericum have been used as traditional medicinal plants in various parts of the world. St. John's Wort (Hypericum perforatum L.) has been reported as an antidepressive, antiviral, antimicrobial, anti-inflammatory, and a healing agent. Thus, extraction with high yields of total extract and high contents of the pharmacological active compounds is desired. St. John's Wort was extracted with supercritical carbon dioxide using a small batch extraction plant. The effects of pressure and temperature were examined with respect to extraction yield of total extract as well as content of two phloroglucinols (hyperforin and adhyperforin). Extracts were analyzed using a highly selective LC/MS/MS method. Applied method of extraction of St. John's Wort was compared with ultrasound extraction with methanol. Supercritical carbon dioxide extraction is showing high selectivity for phloroglucinols. Within the studied range of extraction (pressure: 100, 150, and 200 bar; temperature 40 and 50°C ) a high content of the two phloroglucinols in the resulting extracts was obtained (up to 52%). An increase of extraction temperature showed a negative effect, leading to increased degradation of hyperforin and adhyperforin. Additional, a fractionation of the supercritical carbon dioxide was performed at 100 bar and 40°C. The fraction 250-450 g of consumed CO2 gave the highest content of hyperforin and adhyperforin in extract (up to 54%, for hyperforin).
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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) | 0.001 | 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) | 0.001 | 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".