170 Improving Nutrient Value of Soybean Meal Using Characterized Novel Microbial Fermentation
Bibliographic record
Abstract
Abstract Soybean meal (SBM) is a common plant-based protein source in the swine industry. Its crude protein content is 44 to 50%. However, the presence of antinutritional factors such as high molecular weight, difficult to digest proteins, phytate and non-starch polysaccharides (NSP) limit its use in pigs. We aimed to use fermentation approach to improve the nutrient value of SBM. We have screened, from different sources, and identified bacterial strains that secrete cellulase, xylanase and proteinase as candidates for feedstuff fermentation. Further studies revealed that a few of them are capable of breakdown glycinin and beta-conglycinin, the major proteins in SBM that are difficult to digest and often resulted in intestinal upset in monogastric animal. Molecular characterization revealed that isolate 9 is a Bacillus subtilis strain (B. subtilis 9) secreted high level of cellulase, xylanase, and proteinase comparing to the ATCC control strain and other isolates. It grew significantly faster than the control strain. In addition, it also survived a wide range of concentrations salts and pH. When used for SBM fermentation, protein profiling of fermented SBM using SDS-PAGE revealed a significant reduction of high molecular weight proteins, especially those of glycinin and beta-conglycinin, to small peptides. On a dry matter basis, neutral detergent fiber (NDF) of the fermented SBM was decreased by 34%; crude protein content was increased from 49 to 57%. Pigs fed with a diet formulated with the fermented SBM as the sole source of crude protein had significantly higher apparent ileal digestibility (AID) of dry matter (80 vs. 72 %), ash (56 vs. 36%), crude protein (84 vs. 78%), NDF (71 vs. 66%), and acid detergent fiber (62 vs. 53%) compared with the control pigs fed with unfermented soybean meal. In addition, we have recently explored the possibility of improving feed stuff fermentation efficiency combining microbial species, the effect of this approach will be discussed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".