Trypsin Inhibitor and Urease Activity of Soybean Meal Products from Different Countries and Impact of Trypsin Inhibitor on Ileal Amino Acid Digestibility in Pig
Bibliographic record
Abstract
Abstract Trypsin inhibitor (TI) and urease activity (UA) are the most relevant quality aspects of soybean meal (SBM) for monogastric animals. This study’s objective was to determine the levels of TI and UA in SBM collected from feed mills in different countries, whether TI and UA are correlated since they are both heat‐labile, and impact of TI on pig ileal amino acid digestibility. TI was 1.20–8.37 mg g−1 with 36.4% samples <3 mg g−1, UA was 0–0.311 ΔpH unit with 64.2% samples <0.05 ΔpH unit in 847 solvent‐extracted SBM samples. TI and UA results varied by countries and world areas. By country, mean TI was highest in Germany and lowest in Philippines, mean UA was highest in India and lowest in Philippines. By world areas, mean TI was highest in Latin America and lowest in North America, UA was highest in Asia and lowest in Latin America. TI and UA results vary in different soybean products, TI ranked as raw soybeans > expeller SBM > full‐fat‐extruded (FFE) SBM ≥ heat‐inactivated full‐fat (HIFF) SBM > solvent‐extracted SBM; UA ranked as raw soybeans > HIFF SBM > FFE SBM ≥ solvent‐extracted SBM ≥ expeller SBM. TI was not correlated with UA in 847 solvent‐extracted SBM, suggesting that UA may not be used as a surrogate indicator for TI. A diet contained 38% SBM with 8.78 mg g−1 TI significantly reduced apparent ileal digestibility of amino acids and crude protein by 13.3–26.0% and 23.3% points, respectively, in cannulated pigs compared to SBM with 2.51 mg g−1 TI.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".