Insights into the Potential of Pre-Processing of Ingredients to Improve their Economical Value to Aquaculture Species
Bibliographic record
Abstract
Processed animal protein ingredients are valuable ingredients for aquaculture feed formulations. However, the variability of the chemical composition of different batches of these ingredients and the relatively low digestibility of some of the nutrients (i.e. amino acids, phosphorus) occasionally represent significant limitations for these ingredients at high levels in the diet of some species. Research efforts carried out at the University of Guelph explored the potential of simple and potentially costeffective processing techniques to improve the digestibility and nutritive value of these ingredients. Processing aimed at improving digestibility of phosphorus showed that incubation with different organic acids and a chelating agent and fine grinding significantly improved in vitro bio-availability of bone phosphorus of high ash poultry by-products meal. However, this processing offered no advantage in terms of in vivo digestibility of phosphorus to rainbow trout, a species with an acid stomach. The technique may be useful for species lacking an acid stomach (e.g. carps, shrimp) but this hypothesis has not been verified. In another series of research efforts, incubation of feather meals with protease and a reducing agent, aiming to disrupt residual disulphide bonds and cross-linkage of keratin, significantly improved in vivo digestibility of protein and amino acids and bio-availability of arginine of this ingredient to rainbow trout. The results illustrate the potential of simple processing techniques, based on sound chemical principles, to improve the bio-availability of nutrients of processed animal protein ingredients. However, careful animal assays need to be carried out to confirm the usefulness of these techniques in different species.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".