Removing trophic levels from the fish feed-chain: Evaluating the nutritional and microbiome effects of feeding brewery protein isolate as an alternative to insect meal to Atlantic salmon
Bibliographic record
Abstract
The aim of this study was to associate the net protein losses as well as additional nutritional and microbiome effects of feeding spent brewery grains (SBG) to black solider flies (BSF) that are fed to fish rather than isolating protein from brewery waste using isoelectric precipitation that is directly fed to fish. Following protein loss modelling, a brewery protein isolate (BPI) was produced with a crude protein level of 470 g/kg, which represented an 80 % yield efficiency of protein and was higher that the BSF meal (395 g/kg). Over a three-week cross-over experiment, post-smolt Atlantic salmon ( Salmo salar ) were fed a reference (control) diet or a 30 % substitution with BPI, BSF or soy protein concentrate (SPC) as a control. In addition to the characterisation of the composition of each test ingredient, effects on the nutrient digestibility, palatability and microbiome of each ingredient were assessed. Each diet was fed to a tank of thirty-five salmon (286 ± 20.9 g) for a one-week acclimation period before being stripped of faeces, after which the process repeated twice more to generate three replicates through time. Faeces, diets and ingredients were assessed for protein, and energy digestibilities and by derivation the ingredient digestibilities for these parameters were determined. Feeding the BPI produced in this study to Atlantic salmon showed limited differences in the nutritional value of the BPI compared to the BSF. In addition, both BPI and BSF had beneficial effects on the gut microbiome related to increase in alpha diversity and the abundance of lactic acid bacteria, respectively. Using a systems modelling approach of the net flows of protein from either production method shows that there are massive differences between the potential yields from a BPI approach versus a BSF approach. This has important implications for potential feed protein security, as it shows that from a single tonne of SBG we can get more than a 1000 % difference in protein yields by promoting production of BPI rather than BSF meal production. As such, we suggest that feed grade materials, such as SBG, should not be used in the production of BSF as it represents a substantial loss of protein from the fish feed-chain.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".