<sup>13</sup>C‐NMR Regioisomeric Analysis of EPA and DHA in Fish Oil Derived Triacylglycerol Concentrates
Bibliographic record
Abstract
Abstract The regio‐isomeric distribution of the omega‐3 polyunsaturated fatty acids (PUFA) cis‐5,8,11,14,17‐eicosapentaenoic acid (EPA) and cis‐4,7,10,13,16,19‐docosahexaenoic acid (DHA) in the triacylglycerols (TAG) of anchovy/sardine fish oil was determined by 13C nuclear magnetic resonance (NMR) analysis under quantitative conditions. From the measurements of sn‐1,3 and sn‐2 carbonyl peak areas it was established that EPA was mainly located in the sn‐1,3 positions, whereas DHA primarily occupied the sn‐2 position. Reconstituted TAG prepared by Candida antarctica lipase‐B (CALB) glycerolysis of the ethyl ester (EE) or the free fatty acid (FFA) forms of anchovy/sardine fish oil, displayed a different pattern: EPA was equally distributed, while DHA was preferentially attached to the sn‐1,3 positions. TAG concentrates of varying EPA and DHA molar fractions were prepared by CALB‐catalyzed glycerolysis of the corresponding EE and FFA. 13C‐NMR analysis of the purified products revealed a lack of CALB regioselectivity for EPA and a slight sn‐1,3 regioselectivity for DHA. Since this pattern was observed in all cases of this study, it was concluded that the lipase regioselectivity during TAG synthesis is independent of both the acyl donor type (carboxylic acid or ester) and the fatty acid content of the oil substrate.
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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) | 0.006 | 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".