Optimization of Lipase‐Catalyzed Interesterification of Flaxseed Oil and Tricaprylin Using Response Surface Methodology
Bibliographic record
Abstract
Abstract The biosynthesis of structured lipids (SL) in organic solvent media was carried out by the interesterification of flaxseed oil (FO) and tricaprylin (TC), using Novozym 435. The bioconversion yield (BY, %) of medium‐long‐medium type SL, including C‐caprylic and Ln‐linolenic acids (CLnC), La‐linoleic acid (CLaC) and O‐oleic acid (COC), was monitored. Response surface methodology was used to obtain significant models for the responses, on the basis of a five level, five variable central composite rotatable design. In the experimental preliminary trials significant reaction parameters, including reaction temperature (Tr), TC/FO molar ratio (Mr), enzyme concentration (Ec), reaction time (Rt) and initial water activity (aw), were considered for optimization. Significant models for CLnC, CLaC and COC were determined after regression analysis with backward elimination. The optimal conditions, generated for a maximum CLnC, CLaC and COC, were found to be 54.50–56.25 °C for Tr, 6.23–6.25 mol/mol for Mr, 2.68–3.13 % for Ec, 36.58–37.50 h for Rt and 0.15–0.33 for aw. Under these optimum conditions, the BY of CLnC, CLaC and COC was predicted to be 32.48–36.67, 3.26–3.38 and 5.79–6.16 %, respectively.
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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 | 0.001 | 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 | 0.001 | 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".