Stereospecific quantitation of 6-prenylnaringenin in commercially available H. lupulus-containing natural health products and dietary supplements.
Bibliographic record
Abstract
6-Prenylnaringenin (6PN) is a chiral prenylflavonoid found most prevalently in hops (Humulus lupulus) and present in hops and hop products. It is an isomer of the potent phytoestrogen, 8-prenylnaringenin. An enantiospecific method for quantitation 6PN by LC-ESI-MS has been developed. Baseline enantiomeric resolution of 6PN was attained on a Chiralpak(®) AD-RH column with an isocratic mobile phase consisting of acetonitrile and 10 mM ammonium formate (pH 8.5) (39:61, v/v) and a flow rate of 1.25 mL/min. Quantitative MS data were obtained by selected ion monitoring of the [M-H](-)-ion of both enantiomers of 6PN (m/z 339.10) and the internal standard, 4-acetamidobenzoic acid (m/z 178.05). The method was found to be accurate and precise for enantiospecific quantification of 6PN. The method was successfully applied to the content analysis of 39 commercially available natural health products and dietary supplements reported to contain H. lupulus plant material, extracts and label claims of 6PN. 6PN was present in 25 of 34 products containing plant material or extracts of H. lupulus. Of the five products with claimed amounts of 6PN, all were found to possess <50% of label claims. Results of the content analysis indicated a lack of uniformity in botanical nutraceuticals claiming 6PN content.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".