Optimization of hydrolysis of sardine (<i>Sardina pilchardus</i>) heads with Protamex: enhancement of lipid and phospholipid extraction
Bibliographic record
Abstract
Abstract BACKGROUND: Fish by‐products are not considered as valuable raw materials even if they usually contain valuable components such as lipids. Fish lipids are well known for their nutritional potential and health effects but their extraction remains problematic due to the use of organic solvents. Enzymatic hydrolysis such as the proteolysis of tissues can lead to lipid extraction. RESULTS: Hydrolysis of sardine heads by Protamex was studied (temperature, hydrolysis time and enzyme–substrate ratio) using response surface methodology in order to obtain the highest release of lipids and particularly phospholipids. No relation between the degree of hydrolysis and lipid recovery were depicted; however, optimum conditions for both the release of lipids and phospholipids were found to be similar (29 min, 31 °C with 2.6 g kg−1 enzyme). Under these hydrolysis conditions, rich lipid and phospholipid fractions (oily and aqueous fractions) can be recovered when time, temperature and enzyme consumption are minimized. Analytical data have revealed that they contain high‐quality lipids, especially ω3 fatty acid. CONCLUSION: This study demonstrated that proteolysis can be used for high lipid recovery from little‐exploited biomass like fish heads without requiring solvent or thermal treatment. Resulting phospholipids, fatty acids and peptides could be utilized for nutritional or feed purposes. Copyright © 2009 Society of Chemical Industry
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".