Structured lipids from virgin coconut oil and omega‐3 fatty acids: Process optimization
Bibliographic record
Abstract
Abstract Structured lipids (SLs) containing docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), and DHA + EPA were synthesized via enzymatic acidolysis using virgin coconut oil (VCO) as the substrate in n‐hexane. Commercially available enzymes Lipozyme TL IM (produced from Thermomyces lanuginosus, a 1,3‐specific lipase), Lipozyme IM60 (produced from Rhizomucor miehei, a 1,3‐specific lipase), and non‐specific lipase from Candida rugosa (powder) were used as biocatalysts. The T. lanuginosus lipase was chosen to evaluate the effects of various parameters on the incorporation of PUFAs into VCO and to optimize the process. As the enzyme load increased from 1% to 4%, the incorporation of omega‐3 PUFAs also increased; however, it decreased when the enzyme load was further increased to 6%. The incorporation of these fatty acids increased with reaction time from 12 to 36 h but decreased at 48 h. Similarly, the incorporation increased with temperature from 35 to 45 °C, but decreased at 55 and 65 °C. The highest incorporation rates of DHA (18.91%), EPA (30.38%), and DHA + EPA (34.64%) were achieved at a mole ratio of 1:3 (VCO to DHA or EPA) or 1:3:3 (VCO to DHA + EPA), with a 4% enzyme load, 36 h incubation time, and a temperature of 45 °C. A central composite design (CCD) with three levels and three factors—reaction temperature (35, 45, and 55 °C), enzyme amount (2%, 4%, and 6%), and reaction time (24, 36, and 48 h)—was used to model and optimize the reaction conditions via response surface methodology (RSM). Under optimal conditions of 3.3% T. lanuginosus enzyme, 42.22 °C, and 33.38 h, the incorporation rates were 32.92% for DHA, 44.48% for EPA, and 47.04% for DHA + EPA in VCO.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".