133 Porcine in vitro degradation and fermentation characteristics of heat-pretreated and multi-enzyme-supplemented soybean hulls
Bibliographic record
Abstract
Abstract A study was conducted to determine effects of pretreating and supplementing soybean hulls with multi-enzyme on porcine in vitro digestion and fermentation characteristics. Treatments were untreated and heat-pretreated (160 °C and 70 psi for 20 min) soybean hulls without or with multi-enzyme in a 2 × 2 factorial arrangement. The multi-enzyme supplied 2,800 U of cellulase, 1,800 U of pectinase, 400 U of mannanase, 1,000 U of xylanase, 600 U of glucanase, and 200 U of protease/kilogram of feedstuff. Feedstuffs were subjected to in vitro digestion with porcine pepsin and pancreatin, followed by in vitro fermentation for 72 h. Accumulated gas production was recorded and modeled to estimate kinetics of gas production. On DM basis, untreated and pretreated soybean hulls contained 10.4 and 10.6% CP, and 63.2 and 49.5% ADF, respectively. Pretreatment and multi-enzyme supplementation did not interact on in vitro digestibility of DM (IVDDM). Untreated and pretreated soybean hulls did not differ in IVDDM (24.8 vs. 25.7%). Multi-enzyme increased (P < 0.05) IVDDM of soybean hulls by a mean of 45.5%. Pretreatment and multi-enzyme unaffected total gas production. Pretreatment and multi-enzyme interacted (P < 0.05) on fractional rate of degradation such that the fractional rate of degradation for pretreated soybean hulls was greater (P < 0.05) than that of untreated soybean hulls when soybean hulls were supplemented with multi-enzyme (0.045 vs. 0.062 h-1), but not when soybean hulls were unsupplemented with multi-enzyme (0.053 vs. 0.059 h-1). In conclusion, multi-enzyme supplementation increased IVDDM, implying that the multi-enzyme used in the study can be used to enhance utilization of soybean hulls. Heat pretreatment increased the rate of fermentation of multi-enzyme-supplemented soybean hulls, implying that the rate of fermentation of soybean hulls in the hindgut of pigs can be enhanced by a combination of heat pretreatment and multi-enzyme supplementation.
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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".