Two step-production of acylglycerols containing a high proportion of docosapentaenoic acid from marine omega-3 oil and their oxidative stability
Bibliographic record
Abstract
The objective of this study was to concentrate polyunsaturated fatty acid (PUFA) from seal blubber oil in an innovative manner to produce a high content of docosapentaenoic acid (DPA) in the resultant product. It also aimed at investigating the use of lipases as catalysts for synthesizing acylglycerols from glycerol and polyunsaturated fatty acid concentrates. Additionally, study of the oxidative stability of acylglycerols synthesized by lipases was intended. A two-stage urea complexation process was used to concentrate PUFA from seal blubber oil, giving rise to a DPA content of up to 24.0% in the product. Enzymatic synthesis of acyglycerols directly from glycerol and fatty acid concentrate was studied. Three lipases were used as biocatalysts for esterification. Lipase SP435 from Candida antarctica showed the highest activity for esterification. Effects of reaction parameters, namely temperature, time course and mole ratio of glycerol to fatty acid were followed with all three lipases. The optimal reaction time was 24 hr at 30 °C at a mole ratio of glycerol to fatty acid of 14:1. The maximum degree of acylglycerol synthesis was > 90%. The effect of time course and mole ratio of glycerol to fatty acid on acylglycerols distribution was also determined. The oxidative stability of different samples under Schaal-oven conditions at 60 °C showed that the oxidative stability of acylglycerols was better than that of the corresponding fatty acid esters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".