Revalorization of fish viscera as a sustainable source of proteins, lipids and polysaccharides in the food industry
Bibliographic record
Abstract
Fish viscera, a fish processing by-product, is currently underutilized, despite its complex composition of high value-added components, such as proteins, lipids, and polysaccharides. This work explores the potential for revalorizing fish viscera as a sustainable source of proteins, lipids and polysaccharides in the food industry. Furthermore, their potential food applications and future perspectives are discussed. Fish viscera, constituting 12-18% of the total fish weight, is abundant in proteins, lipids, and polysaccharides. Protein hydrolysates derived from fish viscera exhibit diverse bioactivities, such as antioxidant, ACE-inhibitory, and antibacterial activities. Various enzymes with high stability properties can be extracted from fish viscera. Moreover, fish viscera-derived lipids are abundant in saturated/unsaturated fatty acids and phospholipids, which can exhibit excellent bioactivities such as immune regulatory, anti-inflammatory, lipid metabolism-regulatory and anti-tumor activities. Additionally, polysaccharides present in fish viscera display anticoagulant and antithrombotic activities. Overall, fish viscera have great potential as a good source of proteins, lipids and polysaccharides.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".