Optimization of Medium-Chain Glycerides Enzymatic Synthesis from Crude Palm Kernel Oil and Their Anti-bacterial Potential
Bibliographic record
Abstract
Medium-chain glycerides (MCGs) have been synthesized from crude palm kernel oil (CPKO).CPKO is composed of 77.41% medium-chain fatty acids (MCFA).The synthesis was conducted by reacting CPKO and glycerol, utilizing Candida Antarctica Immobilized Lipase Enzyme B (CALB) as a biocatalyst.This study aims to optimize the enzymatic transesterification to produce an optimal yield of MCGs following Response Surface Methodology (RSM).The optimal MCGs were obtained at a ratio of substrates of 1:3 mole, CALB load of 0.15% wt, and a temperature of 40℃.The optimal MCGs contain 57.61% MGs and 25.24% DGs.FTIR analysis revealed that MCGs exhibited functional groups of -OH, -CH, -CH2, -C=O, -CO, and -C-O-C-.Anti-bacterial examination demonstrated that MCGs at a concentration of 4 mg/mL inhibited the growth of gram-negative bacteria Escherichia coli, with the highest zone of inhibition 11.2 mm observed after 5 days of incubation.In conclusion, MCG is classified as a water-in-oil emulsifier with a hydrophile lipophile balance (HLB) of 3.55 and exhibits anti-bacterial properties.The application of MCGs can prevent food spoilage by microorganisms and increase the shelf life of food products.
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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 | 0.000 |
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".