Determination of phytoestrogens in dietary supplements by LC-MS/MS
Bibliographic record
Abstract
Labelling data quantifying the exact content of individual phytoestrogen analytes in dietary supplements are generally poor. As these products are commonly used in the management of menopause symptoms, any clinical benefits would be dependent on the exact dosage of isoflavones received. Well-established extraction procedures and updated isotope dilution mass spectrometry liquid chromatography coupled with tandem mass spectrometry detection (LC-MS/MS) have been used to accurately quantify the concentrations of ten common isoflavones in 35 dietary supplement samples on sale in the UK, Canada and Italy. Concentration-specific ionization suppression is described for biochanin A and formononetin. All supplements contained phytoestrogens. The soya isoflavones (genistein, daidzein, glycitein) were present in all products and the majority also contained the red clover isoflavones (biochanin A, formononetin) and some the Kudzu isoflavones (daidzein, puerarin). The content of total isoflavones per dose ranged from <1 to 53 mg. Trace amounts of coumestrol were found in six products. Other less common analytes, the prenylnaringenins (6-prenylnaringenin, 8-prenylnaringenin, 6,8-diprenylnaringenin) were not found in any of the products. Only 14 of 35 supplements were found to deliver more than or equal to 40 mg day(-1) of aglycone isoflavones, a consensus dose value recognized as delivering therapeutic benefit. Eleven did not match label claims. Six delivered less than 10 mg day (-1) of isoflavones. There has been little improvement in the overall quality of industry labelling in the five years since this was last investigated. Consequently, the public, retailers and healthcare professionals should consider using standardized isoflavone supplements, which are supported by analytical measurements.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".