Enzymatic Synthesis of Sucrose Esters from Off-Grade Palm Kernel Oil Methyl Ester: Characterization and Application as a Food Emulsifier
Bibliographic record
Abstract
Sucrose esters (SEs) are emulsifiers utilized in food products to enhance flavor, color, and texture.This study focused on the synthesis of SEs from crude palm kernel oil methyl ester (CPKOME) and sucrose using Candida antarctica lipase B (CALB).CPKOME was derived from off-grade crude palm kernel oil as a byproduct of the palm oil industry.The SEs synthesis was conducted at 30℃, a CALB load of 50 mg, with a stirring speed of 250 rpm.The crude SEs were separated through vacuum filtration and dried at 40℃ for 6 hours.This study aimed to determine the optimum substrate conversion based on reaction time and sucrose concentration.The results showed an optimal substrate conversion of 87.80% (SEs weight of 84.42 g), which was achieved at a sucrose concentration of 400 mg.mL -1 and a reaction time of 12 h.The characteristics of the SEs with optimal conversion obtained were as follows: purity of 91.82%, free sucrose of 1.75%, acid value of 2.93, and free methanol of 8.92 mg• kg -1 .SEs have a hydrophiliclipophilic balance (HLB) of 12.35 and are classified as oil-in-water (O/W) emulsifiers.The range of HLB values that are considered appropriate to stabilize oil-in-water emulsions is between 8 and 18. Applying SEs of 0.6% (w/v) in a coconut milk emulsion can maintain emulsion stability for 7 days of storage.In addition, SEs showed antibacterial activity by inhibiting the growth of Staphylococcus aureus.At a concentration of 1 mg• mL -1 , SEs showed an inhibition zone of 7.24 mm after 5 days of incubation.In comparison, gentamicin (positive control) at the same concentration showed an inhibition zone of 24.90 mm.Moreover, this study demonstrates a sustainable enzymatic route to convert off-grade crude palm kernel oil into SEs, which have potential as multifunctional food emulsifiers and antibacterial agents.
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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".