Pressurized water extraction of naphtodianthrones in St. John's wort (Hypericum perforatum L.)
Bibliographic record
Abstract
A high pressure water extraction (PWE) method was developed for separation of naphtodianthrones (hypericin, protohypericin, pseudohypericin, and protopseudohypericin) in St. John's wort. The effects of extraction temperature, pressure, pH, particle size, and modifier were studied, and the PWE method was compared to a conventional solvent extraction method. The most important factors affecting the extraction efficiency were found to be temperature, pH and particle size. The highest extraction efficiency of naphtodianthrones was achieved when the extraction was performed using a fine sample powder (≤80 mesh), a low temperature (ambient), and 100 bar pressure at pH ≥ 7. Using these conditions the PWE efficiency to extract naphtodianthrones (the summed amount of hypericin, pseudohypericin and their protoforms) compared to methanol extraction (with ultrasound) was about 80% (90% pseudohypericin and 60% hypericin). PWE extraction efficiency was further improved when ethanol was added to the sample as a modifier before the extraction. Using ethanol modified PWE at pH 7 the summed amount of naphtodianthrones was 90% (95% pseudohypericin and over 80% hypericin ) compared to the methanol extraction with ultrasound. Reproducibility of the optimized PWE method was comparatively good; the relative standard deviation of both hypericin and pseudohypericin was 8%.
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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".