Structured lipids from high‐laurate canola oil and long‐chain omega‐3 fatty acids
Bibliographic record
Abstract
Abstract The lipase‐assisted acidolysis of high‐laurate canola oil (HLCO; Laurical 25) with long‐chain n−3 FA (DHA and EPA) was studied. Response surface methodology was used to obtain a maximal incorporation of DHA or EPA into HLCO. The studied process variables were the amount of enzyme (2–6%), reaction temperature (35–55°C), and incubation time (12–36 h). The amount of water added and the mole ratio of substrates (oil to DHA or EPA) were kept at 2% and 1∶3, respectively. All experiments were conducted according to a face‐centered cube design. Under optimal conditions (4.79% of enzyme; 46.1°C; 30.1 h), the incorporation of DHA into HLCO was 37.3%. The corresponding maximal incorporation of EPA (61.6%) into Laurical 25 was obtained using 4.6% enzyme, a reaction temperature of 39.9°C, and a reaction period of 26.2 h. Examination of the positional distribution of FA on the glycerol backbone of modified HLCO with DHA showed that the DHA was primarily located in the sn‐1,3 positions of the TAG molecules. However, lauric acid also remained mainly in the sn‐1,3 positions of the modified oil. For EPA‐modified Laurical 25, lauric acid was present mainly in the sn‐1,3 positions, whereas EPA was randomly distributed over the three positions.
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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 | 0.000 |
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".