Kinetic mechanism of conjugated linoleic acid esterification and production of enriched glycerides as functional oil
Bibliographic record
Abstract
Abstract Experimental determination of the effects of conjugated linoleic acid (CLA) and glycerol on the rate of enzymatic transesterification were studied to propose suitable mechanistic steps and generate a kinetic model. CLA was suggested due to its purported health benefits and application in preparation of functional foods. CLA, glycerol, and sunflower oil blends with varying concentrations were reacted using a 1, 3‐specific immobilized lipase from Rhizomucor mehei. Scrutiny for mass transfer effects showed that esterification reaction was kinetically controlled. The reaction rate was determined, which showed that affinity of enzyme to CLA is lower than to glycerol. The transesterified lipids were analyzed by gas chromatography for composition of fatty acids and were evaluated for the free fatty acids (FFA). The esterification reaction kinetic follows the Ping Pong Bi Bi mechanism with competitive full (dead end) inhibition by acyl acceptors characterized by the Vmax, KmCOOH, KmG, and KIG values of 0.328 (mol/L/h), 0.342 (mol/L), 0.04526 (mol/L), and 0.329 (mol/L), respectively. Kinetic model study results indicated how FFAs (CLA and other FFAs) and triacylglycerols (sunflower molecules) can participate as the first substrate in the reaction to produce enriched triacylglycerols as the functional 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) | 0.000 | 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 | 0.001 |
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".