Isolation of bacteria from fermented food and grass carp intestine and their efficiencies in improving nutrient value of soybean meal in solid state fermentation
Bibliographic record
Abstract
Soybean meal is an excellent and cost-effective protein source; however, its usage is limited in the piglet due to the presence of anti-nutritional factors and the antigens glycinin and β-conglycinin. The objective of the current study was to screen and select for bacteria that can be efficiently adopted to ferment soybean meal in order to solve this problem. Bacteria were isolated from fermented soy foods and the grass carp intestine, and strains selected for high protease, cellulase and amylase activities. The isolated bacteria were characterized as Bacillus cereus, Bacillus subtilis and Bacilus amyloliquefacien, respectively. Fermentation with food-derived Isolate-2 and fish-derived F-9 increased crude protein content by 5.32% and 8.27%, respectively; improved the amino acid profile by increasing certain essential amino acids, broke down larger soy protein to 35 kDa and under, eliminated antigenicity against glycinin and β-conglycinin, and removed raffinose and stachyose in the soybean meal following a 24-h fermentation. Our results suggest these two B. amyloliquefaciens bacteria can efficiently solid state ferment soybean meal and ultimately produce a more utilizable food source for growing healthy piglets.
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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) | 0.001 | 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 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".