Fish By‐Products Utilization in Food and Health: Extraction Technologies, Bioactive, and Sustainability Challenges
Bibliographic record
Abstract
Fish by-products, traditionally regarded as waste, are increasingly recognized as valuable sources of bioactive compounds, including peptides, omega-3 fatty acids, collagen, and hydroxyapatite. These molecules exhibit significant functional properties with applications in food preservation, dietary supplementation, pharmaceuticals, and cosmeceuticals. This review explores advanced extraction technologies such as enzyme-assisted hydrolysis, supercritical fluid extraction, and cold plasma processing, which enhance the yield and stability of bioactives while supporting zero-waste and circular economy principles. Despite technological progress, key barriers remain, including inconsistent raw material quality, high processing costs, regulatory uncertainty, and limited industrial infrastructure. Peptides and protein hydrolysates derived from fish frames, skins, viscera, and scales have demonstrated antioxidant, antihypertensive, antimicrobial, antidiabetic activities, but translation into functional food and health products is constrained by scalability and regulatory challenges. Future work should focus on optimizing bioprocessing, validating health benefits through clinical trials, and implementing sustainable valorization frameworks. Addressing these challenges will unlock the full potential of fish by-products in advancing food security and human health.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".