Enzymes enhance degradation of the fiber–starch–protein matrix of distillers dried grains with solubles as revealed by a porcine in vitro fermentation model and microscopy
Bibliographic record
Abstract
Effects of treating corn and wheat distillers dried grains with solubles (DDGS) with a multicarbohydrase alone or in combination with a protease on porcine in vitro fermentation characteristics and the matrix structure of the DGGS before and after the fermentation were studied. Three DDGS samples (wheat DDGS sample 1 [wDDGS1], wheat DDGS sample 2 [wDDGS2], and corn DDGS [cDDGS]) were predigested with pepsin and pancreatin. Residues were then subjected to in vitro fermentation using buffered mineral solution inoculated with fresh pig feces without or with a multicarbohydrase alone or in combination with protease in a 3 × 3 factorial arrangement. Accumulated gas production was measured for up to 72 h. Concentration of VFA was measured in fermented solutions. The matrix of native DDGS and their residues after fermentation was analyzed using confocal laser scanning microscopy and scanning electron microscopy to determine internal and external structures, respectively. On a DM basis, wDDGS1, wDDGS2, and cDDGS contained 35.5, 43.4, and 29.0% CP; 2.23, 0.51, and 6.40% starch; 0.82, 0.80, and 0.89% available Lys; and 24.8, 22.5, and 23.0% total nonstarch polysaccharides, respectively. The in vitro digestibility of DM for wDDGS1, wDDGS2, and cDDGS was 67.7, 72.1, and 59.6%, respectively. The cDDGS had greater ( < 0.05) total gas and VFA production than both wheat DDGS. The wDDGS2 had lower ( < 0.05) total gas production than wDDGS1. Multicarbohydrase increased ( < 0.05) total gas production for cDDGS and total VFA production for wDGGS1 but did not increase gas or VFA production for wDDGS2. Addition of protease with multicarbohydrase to DDGS reduced ( < 0.05) total gas and VFA productions and increased ( < 0.05) branched-chain VFA regardless of DDGS type. Confocal laser scanning microscopy and scanning electron microscopy revealed that DDGS were mainly aggregates of resistant and nonfermentable starchy and nonstarchy complexes formed during DDGS production. After in vitro fermentation with porcine fecal inoculum, particles of enzyme-treated DDGS were generally smaller than those of the untreated DDGS. In conclusion, cDDGS had a more porous matrix that was more fermentable than the wheat DDGS. The wDDGS2 was less fermentable than wDDGS1. Multicarbohydrase increased fermentability of cDDGS and wDDGS1 but not wDDGS2, indicating that its efficacy in DDGS is dependent on matrix porosity and DDGS source. Protease hindered efficacy of multicarbohydrase.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".