Structured Lipids via Lipase-Catalyzed Incorporation of Eicosapentaenoic Acid into Borage (<i>Borago officinalis</i> L.) and Evening Primrose (<i>Oenothera biennis</i> L.) Oils
Bibliographic record
Abstract
Enzymatic acidolysis of borage oil (BO) or evening primrose oil (EPO) with eicosapentaenoic acid (20:5n-3; EPA) was studied. Of the six lipases that were tested in the initial screening, nonspecific lipase PS-30 from Pseudomonas sp. resulted in the highest incorporation of EPA into both oils. This enzyme was further studied for the influence of enzyme load, temperature, time, type of organic solvent, and mole ratio of substrates. The products from the acidolysis reaction were analyzed by gas chromatography (GC). The highest incorporation of EPA in both oils occurred at 45-55 degrees C and at 150-250 enzyme activity units. One unit of lipase activity was defined as nanomoles of fatty acids (oleic acid equivalents) produced per minute per gram of enzyme. Time course studies indicated that EPA incorporation was increased up to 26.8 and 25.2% (after 24 h) in BO and EPO, respectively. Among the solvents examined, n-hexane served best for the acidolysis of EPA with both oils. The effect of the mole ratio of oil to EPA was studied from 1:1 to 1:3. As the mole ratio of EPA increased, the incorporation increased from 25.2-26.8 to 37.4-39.9% (after 24 h). The highest EPA incorporations of 39.9 and 37.4% in BO and EPO, respectively, occurred at the stoichiometric mole ratio of 1:3 for oil to EPA.
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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) | 0.000 | 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".