EXTRACTION OF OMEGA-3 FATTY ACIDS FROM ATLANTIC HERRING ( Clupea harengus)
Bibliographic record
Abstract
The fish processing industry faces problems due to the negative environmental impact of its wastes. Fish wastes can be used to produce oil, protein, calcium, bio-diesel and numerous bioactive compounds. In particular, fish oil is rich in omega-3 fatty acids which can aid prevention and treatment of various diseases. The challenge is to develop environmentally friendly and economically feasible techniques for extracting omega-3 fatty acids. In this study, extraction of fish oil and omega-3 fatty acids from Atlantic herring (Clupea harengus) by enzymatic hydrolysis was studied. The effects of temperature, pH and enzyme loads on the extraction of fish oil using enzyme alcalase were evaluated; the effect of mixing on the extraction of omega-3 fatty acids was also investigated. Fatty acids were categorized into three groups: saturated (SFA), monounsaturated (MUFA) and polyunsaturated fatty acids (PUFA). Increasing the reaction temperature decreased the recovery yield (RY), and increased peroxide value (PV), acid value (AV), p-anisidine value (p-AV), SFA, PUFA and MUFA. Increasing the pH up to 7.5 decreased PV and increased AV, p-AV, RY, SFA, MUFA and PUFA. Generally, increasing enzyme load increased RY, PV, AV, p-AV. However, few samples showed increased SFA, MUFA and PUFA with increased enzyme load. The optimum conditions of enzymatic extraction were at 55°C, pH of 7.5 and enzyme load of 2.0%. Concentration of omega-3 fatty acid should be carried out with immobilized enzymes and a higher centrifugal speed should be used to separate the oil after hydrolysis. Also, antioxidants should be used to avoid oil oxidation. A constant fast stirring (200 rpm) gave the highest omega-3 fatty acid content. The results showed that solvent extraction was better (RY of 97.5%, PV of 12.04 Meq/g, AV of 5.11 mg KOH/g and p-AV of 9.33) compared to enzymatic extraction (RY of 20.2%, PV of 31.44 Meq/g, AV of 11.13 mg KOH/g and p-AV of 58.27). Extraction of fish oil using solvent system can be used but choloroform-methanol should be replaced with hexane-isopropanol.
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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 | 0.001 | 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) | 0.000 | 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".