Synthesis of acylglycerols from ω−3 fatty acids and conjugated linoleic acid isomers
Bibliographic record
Abstract
n-3 PUFA (omega-3 polyunsaturated fatty acid) concentrate from mackerel oil enriched in EPA (eicosapentaenoic acid) and DHA (docosahexaenoic acid) was used to esterify isomers of CLA (conjuated linoleic acid) to produce acylglycerols (glycerides). Catalysis was potentiated by immobilized lipases from the yeast Candida antarctica and the mould Mucor miehei. C. antarctica lipase showed higher reactivity and much faster initial rate of incorporation of CLA into acylglycerols than its M. miehei counterpart. Synthesis with molecular sieves also achieved a better rate of incorporation of fatty acids into acylglycerols than using vacuum or systems without water removal. Esterification achieved at 40 degrees C with C. antarctica lipase was significantly different from that achieved at 60 or 50 degrees C. However, there was no significant (P<0.05) difference between esterification at 50 degrees C and 60 degrees C. The molar ratio of glycerol to fatty acid was found to influence the initial rate of incorporation, with the lower ratios showing higher initial rates compared with the higher ratios. The present study shows that direct esterification is an effective mechanism for producing acylglycerols from fatty acids in a controlled system.
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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 | 0.001 |
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".