ENZYMATIC ACIDOLYSIS OF EVENING PRIMROSE OIL WITH DOCOSAHEXAENOIC ACID USING RESPONSE SURFACE METHODOLOGY
Bibliographic record
Abstract
ABSTRACT Lipase‐catalyzed acidolysis of evening primrose oil with docosahexaenoic acid (DHA) was carried out using Novozym‐435 from Candida antarctica as the biocatalyst. Several parameters, namely the amount of enzyme (X1; 100–200 enzyme activity units), incubation temperature (X2; 30–60C) and reaction time (X3; 18–30 h) were examined as independent variables of face‐centered cube design. Using response surface methodology (RSM), the optimum reaction conditions for DHA incorporation into evening primrose oil were determined and quadratic response surface was drawn from the mathematical model. The results demonstrated that the amount of enzyme, reaction temperature and reaction time significantly (P ≤ 0.05) affected the incorporation of DHA into evening primrose oil. Optimum reaction conditions for DHA incorporation were 162 units of enzyme at 43C in 27 h. The optimum value predicted by RSM for DHA incorporation was 31.9%. Analysis of variance showed that 95% (R2 = 0.95) of the observed variation was explained by the polynomial model. Close agreement existed between experimental and predicted values. The positional distribution of fatty acids in DHA‐enriched evening primrose indicated that DHA was randomly distributed among the sn‐2 and sn‐1 + sn‐3 positions of the DHA‐enriched evening primrose oil.
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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) | 0.001 | 0.001 |
| 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".