Characterization of lactoferrin oil-in-water emulsions and their stability in recombined milk
Bibliographic record
Abstract
Emulsions were prepared with 20% soy oil and different concentrations of lactoferrin, and tested at pH values from 3 to 7·5. The stability of the emulsions decreased as the pH got closer to the isoelectric point of the protein. A concentration of 1% lactoferrin was determined to be sufficient to provide full coverage of the emulsion droplets. Lactoferrin-stabilized emulsions were then prepared in water at pH 6·6 and their behaviour when added to reconstituted milk was studied. It was observed that lactoferrin emulsions were stable when reconstituted in milk, but they showed aggregation when diluted in milk serum alone. The destabilization was caused by shielding of the charges on the surface of the oil droplets. Stabilization in milk occurred due to interactions at the interface with other soluble proteins. In fact, when β-lactoglobulin or sodium caseinate were added to the serum, stability of the emulsion droplets was restored, indicating that these proteins were able to adsorb at the interface and aid in the stabilization. Since ζ-potential measurements did not show significant overall charge on the emulsion droplets, this suggests that the stabilization forces are not only electrostatic in nature, but that there are other mechanisms at play.
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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 | 0.001 |
| 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".