In Vitro Assessment of the Effectiveness of Mineral Adsorbents in Sequestering Boar Taint Compounds
Bibliographic record
Abstract
The utility of four mineral adsorbents as potential feed additives to bind the boar taint compounds, androstenone and skatole, was assessed with an in vitro system. The adsorbents were bentonite (BNT), diatomaceous earth (DE), spent filter aid (SFA) and hydrated sodium–calcium aluminosilicate (HSCAS), with activated charcoal (AC) as a positive control. The binding capacity (Bmax) and binding affinity (K) of androstenone (AND), estrone (E1), estrone sulfate (E1S), and skatole were estimated using the modified Michaelis–Menten kinetics. The Langmuir and Freundlich isotherm models were also used to assess the adsorption behaviour. The Bmax values with AND were 77.7 ± 1.12%, 71.9 ± 1.93%, 55.0 ± 7.85%, and 69.5 ± 1.39% for BNT, DE, SFA, and HSCAS, respectively, with no differences in the binding affinity K (p > 0.05). All the mineral adsorbents had very low binding with E1S. SFA bound skatole with a Bmax of 89.9 ± 1.09%, while the Bmax values for skatole binding by BNT, DE and HCAS were approximately 15%. Most adsorbent–adsorbate complexes fit best with the Freundlich isotherm model. We conclude that all four mineral adsorbents bound androstenone, but not E1S, and only SFA effectively bound skatole. This suggests that SFA may act as a selective dietary binding agent to control boar taint, but further research using animal models is needed to explore the utility and selectivity of these adsorbents as feed additives to control 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".