Salmon processing discards: a potential source of bioactive peptides – a review
Bibliographic record
Abstract
Abstract Salmon aquaculture generates 80% of the total revenue of finfish aquaculture across Canada. Salmon farming is carried out in a multilevel process, and at least 60% of the total production is considered as by-products, including skin, head, viscera, trimmings, frames, bones, and roes. These by-products are an excellent source of protein, which can be converted to protein hydrolysates through enzymatic hydrolysis and non-enzymatic processes such as chemical hydrolysis (acid and alkaline) in order to utilize them into value-added products. Several studies have reported that peptides from salmon protein hydrolysates possess bioactivities, including antihypertensive, antioxidant, anticancer, antimicrobial, antidiabetic, anti-allergic, and cholesterol-lowering effects. Incorporating in silico computational methods is gaining more attention to identify potential peptides from source proteins. The in silico methods can be used to predict the properties of the peptides and thereby predetermine the processing, isolation, and purification steps that can be used for the peptides of interest. Therefore, it is essential to implement robust, standardized, and cost-effective processing techniques that can easily be transferrable and scale up for industrial applications in view of circular economy and upcycling concept. This contribution summarizes the latest research information on Atlantic salmon, production statistics, growth lifecycle, processing, protein production techniques, nutritional and functional properties, peptide production and purification processes, as well as potential health benefits as a nutraceutical product. Graphical Abstract
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.006 | 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".