Development of a Novel Enzymatic Pretreatment for Improving the Digestibility of Protein in Feather Meal
Bibliographic record
Abstract
This study describes the process of developing an enzymatic pretreatment to improve the nutritional value of feather meal (FeM). In a first experiment, a full factorial design was used to examine the effects of various incubation conditions on the solubilization of nitrogen in FeM. We incubated FeM for 3 h with various levels of a commercial alkaline serine protease (Savinase® 16L), sodium sulphite (Na2SO3), and digestion buffer. A Savinase® 16L level of 3% (%FeM v/w), Na2SO3 level of 3% (%FeM w/w), and digestion buffer level of 500% (%FeM w/w) were identified as the optimal conditions. Under these optimal conditions, 45% of the nitrogen in FeM was solubilized. In a second experiment, we evaluated the effect of more economically sustainable incubation conditions on the in vitro digestibility of protein (pepsin-HCl digestibility and multistep protein evaluation) in FeM. Two FeMs were incubated with 0.5% Savinase® 16L (%FeM v/w), 2% Na2SO3 (%FeM w/w), and 200% buffer (%FeM w/w) for 24 h. The pretreatment improved pepsin-HCl digestibility by 7–16% and the total tract degradable protein content by 14–50%. Accordingly, this novel pretreatment could be applied in the animal feed industry to improve the nutritional value of FeM.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".