Simultaneous Determination of the Predominant Hyperforins and Hypericins in St. John's Wort (Hypericum perforatum L.) by Liquid Chromatography
Bibliographic record
Abstract
Hypericin and hyperforin are believed to be among the active constituents in common St. John's wort (Hypericum perforatum L.). Presently, dietary supplements are generally standardized to contain specified levels of hypericin and hyperforin, and the related compounds, pseudohypericin and adhyperforin. A rapid method was developed for simultaneous determination of these 4 active constituents by liquid chromatography (LC). A 1 g portion of dried, finely ground leaf/flower sample is extracted with 20 mL methanol for 2 h. A 0.6 mL aliquot of the crude extract is combined with 5.4 mL acetonitrile-methanol (9 + 1) and passed through a mixed solid-phase cleanup column. The eluate is examined by LC for hyperforin, adhyperforin, hypericin, and pseudohypericin on a Hypersil reversed-phase column by using simultaneous ultraviolet (284 nm) and fluorescence detection (excitation, 470 nm; emission, 590 nm). The compounds are easily separated isocratically within 8 min with a mobile phase of acetonitrile-aqueous 0.1 M triethylammonium acetate (8 + 2). Average recoveries of hyperforin and adhyperforin were 101.9 and 98.4%, respectively, for 3 sample mixtures containing concentrations ranging from approximately 0.2 to 1.5% combined hyperforins per gram dry weight. Average relative standard deviation (RSD) values for hyperforin and adhyperforin for all 3 mixtures were 18.9 and 18.0%, respectively. Average recoveries of hypericin and pseudohypericin were 88.6 and 93.3% respectively, from 3 sample mixtures containing concentrations ranging from approximately 0.2 to 0.4% combined hypericins per gram dry weight. Average RSD values for hypericin and pseudohypericin for all 3 mixtures were 3.8 and 4.2%, respectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".