Effect of Cooking on Concentrations of β-Estradiol and Metabolites in Model Matrices and Beef
Bibliographic record
Abstract
Because beef food products are generally cooked prior to consumption, the behavior of chemicals in these cooked foods is important in estimating human exposure. The heat stability of the natural estrogen β-estradiol (β-E2) and its metabolites α-estradiol (α-E2), estrone (E1), and several catechol estrogens was examined in heated vegetable oil and aqueous solutions. The chemicals were also incorporated into regular and extra lean ground beef and subjected to cooking. E1 and E2 were stable in aqueous solutions at 100°C, whereas the catechol estrogens exhibited first-order decay curves with half-lives of 2-10 min. Their stability improved to the same level as the other test chemicals when an antioxidant was added to the solution, suggesting that their disappearance was due to oxidation rather than thermal degradation. E1 and E2 were also stable in heated vegetable oil (160-180°C), whereas catechol estrogen decreased 30-50% over the 2 h duration of the experiments. Chemical losses from cooked beef appear to be related to the fat content of the beef, with greater losses occurring in regular ground beef (25-30%), compared to extra lean ground beef (5-20%). This study shows that cooking reduces but does not eliminate the potential for dietary exposure to growth promoters in ground beef.
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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) | 0.000 | 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) | 0.001 | 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".