Stabilization of Oil-in-Water Emulsions by β-Lactoglobulin−Polyethylene Glycol Conjugates
Bibliographic record
Abstract
The disulfide bonds of beta-lactoglobulin (beta-lg) were modified by oxidative sulfitolysis to generate beta-lgSO(3). The native protein (beta-lg) and the modified protein (beta-lgSO(3)) were conjugated to activated polyethylene glycol (PEG) to generate beta-lgPEG and beta-lgSO(3)PEG, respectively. Oil-in-water (o/w) emulsions containing 1% beta-lg or beta-lg conjugates were prepared at pH 2.8, 5.0, and 7.0. Emulsion droplet diameters and zeta potentials were measured. For the same emulsifier, emulsion droplet diameters decreased when emulsion pH increased. Zeta potentials of emulsion droplets increased with pH for beta-lg and beta-lgSO(3). Zeta potentials of beta-lgPEG and beta-lgSO(3)PEG approached zero, suggesting that the protein molecule was covered by PEG chains. Accelerated and 7-day storage stabilities at 21 degrees C of the emulsions were monitored. The emulsifying activity index (EAI) of beta-lgPEG was not significantly different from the EAI of beta-lg. The EAI of beta-lg was enhanced following sulfitolysis of beta-lactoglobulin. The emulsifying activity increased more when the oxidatively modified protein was conjugated to polyethylene glycol. Emulsions made with beta-lgSO(3)PEG were more stable than emulsions made with beta-lg, beta-lgPEG, or beta-lgSO(3) under accelerated stability study and for 7 days at 21 degrees C. The stability of o/w emulsions stabilized with beta-lgSO(3)PEG increased because individual droplets were better protected, against protein bridging or coalescence, by the thick adsorbed protein-PEG layer.
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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) | 0.000 | 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".