Liquid Chromatographic Determination of St. John's Wort Components in Functional Foods
Bibliographic record
Abstract
A method was developed for determination of St. John's wort marker compounds hypericin, pseudohypericin, hyperforin, and adhyperforin in functional foods. Solid-phase extraction provided analyte extraction and significant sample cleanup prior to analysis using liquid chromatography (LC) with UV and fluorescence detection. In addition to quantification using LC-UV, confirmation was made with electrospray ionization LC mass spectrometry (LC/MS). Several commercially available tea and drink products claiming to contain St. John's wort were tested. Recoveries ranged from 51 to 98% for the liquid samples. Comparison of the concentrations in 4 St. John's wort teas showed a variation in analyte concentration (1044-10 ng/mL marker compounds in brewed tea) and composition. No marker compounds were found in the beverages, indicating possible decomposition of the marker compounds caused by low pH and/or exposure to light. A solvent extraction procedure was developed for analysis of the marker compounds from solid samples. Analytes were detected at low parts per million, with an average recovery of 75%. No St. John's wort components were found in the 2 solid functional food samples analyzed.
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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 | 0.000 |
| 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) | 0.001 | 0.001 |
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".