PSIII-17 Extrusion Enhances Nutrient and Energy Digestibility of Pulse Grain-based Diets Fed to Growing Pigs
Bibliographic record
Abstract
Abstract Heat processing may enhance nutrient digestibility of diets based on pulse grains. The objective of this study was to determine nutrient and energy digestibility of extruded lentil-based diets containing either supplemental plant or animal protein source in growing pigs. Two diets were formulated to provide 2.4 Mcal NE/kg and 4.35 g standardized ileal digestible Lys/Mcal NE: 1) soybean meal diet (SBM), containing 50% lentil, 31% wheat, and 12.8% soybean meal; and 2) fish meal diet (FM), containing 40% lentil, 45% wheat, and 10% fish meal. Following mixing, each diet was divided into 2 parts: one part remained mash while the other part was extruded using a single-screw extruder (105°C, 400 rpm, 5 kg water/h). The 2 extruded and 2 non-extruded diets were tested in a 2 × 2 factorial arrangement. Eight ileal-cannulated barrows (32.3 kg) were fed the 4 diets at 2.8 times maintenance DE (110 kcal per kg of BW0.75) for four 9-d periods in a double 4 × 4 Latin square. The FM diets had greater (P < 0.05) AID of DM, GE, and most AA, and ATTD of CP, but lower apparent hindgut fermentation of DM and GE than the SBM diets. The AID of CP and AA were 3.2 and 4.7%-units greater (P < 0.05), respectively, and the ATTD of GE and DE values were 2.1 and 3.1%-units greater (P < 0.05), respectively, for the extruded diets than the non-extruded diets. Interactions between protein source and extrusion were not observed. In conclusion, FM diets had greater ileal digestibility of DM, energy, and AA than SBM diets. Extrusion increased the AID of CP and most AA, and DE value of both plant- and animal-protein diets based on lentil grain, indicating that extrusion can increase the energy and protein value of pulse-grain based diets fed to growing pigs.
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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 | 0.000 |
| 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".