A New UHPLC Analytical Method for St. John’s Wort (Hypericum perforatum) Extracts
Bibliographic record
Abstract
Commonly used to treat mood disorders, St. John’s Wort (Hypericum perforatum) is a popular herb in the natural health products industry. The potency of its active ingredients can be determined using a number of different analytical methods, but it is more widely determined using high performance liquid chromatography (HPLC). While monographs in the United States Pharmacopeia (USP) can often be relied upon for suitable analytical methods, the method proposed for determining hypericin content in St. John’s Wort products is inefficient in carrying out this purpose. This paper presents a modified new HPLC method for determining the hypericin content that can also be used for St. John’s Worts capsules and tablets by making use of purified hypericin as a chemical standard instead of oxybenzone, applying a wavelength of 588 nm during analysis and utilizing a binary instead of ternary mobile phase gradient. The resulting method and sample chromatograms provide better resolved, more easily identifiable peaks, shorter run time, and increased sustainability compared to the original USP method. This proposed method was developed using the more refined ultra-high performance liquid chromatography (UHPLC) and serves as a more accurate and reliable method for determining hypericin content in St. John’s Wort.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".