In vitro assessment of the effectiveness of non-nutritive sorbent materials as binding agents for boar taint compounds
Bibliographic record
Abstract
Boar taint, an off-odor and an off-flavor in the meat from some uncastrated male pigs, is due to high levels of the testicular steroid hormone, androstenone, and the indole, skatole. Thus far, there are no known methods for controlling both androstenone and skatole through dietary means. We tested the adsorbent agents, cholestyramine (CH), activated carbon (AC), tween-60 (Tween), bentonite (BNT) and polyvinylpolypyrrolidone (PVPP) for binding androstenone, estrone (E(1)), estrone sulfate (E(1)S) and skatole from buffer solutions in an in vitro system. The goal was to determine the potential utility of these binding agents as feed additives to control boar taint. Michaelis-Menten analysis was utilized to determine the effectiveness of the adsorbents. At pH 7.4, E(1)S was bound to AC and CH with the highest B(max) (maximum binding), whereas Tween and AC had the greatest B(max) for E(1). The B(max) for skatole at pH 7.4 was highest for AC, CH and PVPP. AC had a higher B(max) for androstenone than CH and Tween. The B(max) values at pH 3.0 with E(1)S for AC and CH were essentially 100%, whereas the binding of Tween to E(1)S at pH 3.0 decreased by 49.5% from binding at pH 7.4 (P < 0.05). The Ad(int) values, which represent efficiency of binding, illustrated that AC bound E(1), androstenone and skatole with greater efficiency than the other binding agents at pH 7.4, whereas AC bound E(1)S as efficiently as CH. We conclude that AC was the most effective adsorbent agent for binding E(1), E(1)S, androstenone and skatole in vitro, followed by CH, Tween, PVPP and lastly BNT. These adsorbent agents may be useful for binding boar taint compounds in in vivo studies to decrease the risk of boar taint.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".