St. John's Wort versus Counterfeit St. John's Wort: An HPTLC Study
Bibliographic record
Abstract
Hypericum perforatum L. is the most commonly used herb for treating depression. Due to the popularity of this botanical, there is a potential for economically driven adulteration of St. John's wort (SJW) products. The goal of this study was to investigate SJW ingredients suspected to be adulterated based on simple preliminary HPTLC tests. Commercial samples were analyzed by HPTLC following the United States Pharmacopeia (USP) monograph methodology, with additional visualization under white light. A number of these samples presented odd methanolic solution colors and unconventional HPTLC fingerprints, suggesting the presence of other species and/or extraneous polar additives. To achieve identification and separation of the polar additives, a new reversed-phase HPTLC method was developed. The adulterants were identified as synthetic dyes in the amounts of 0.51 to 1.36% by weight. Identities of the dyes were confirmed by scanning densitometry and HPTLC-MS. A modified USP method with additional detection mode permitted the identification of eight SJW samples adulterated with dyes and six others with flavonoid fingerprints different from those specified by USP from a total of 37 samples of dry extracts, finished products, and bulk raw herb. A decision flowchart is proposed to guide the detection of adulteration of SJW in a systematic fashion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.002 | 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".