Stabilization of Caseinate-Covered Oil Droplets during Acidification with High Methoxyl Pectin
Bibliographic record
Abstract
Polysaccharides are widely used in the food industry to modify the stability of protein-based drinks. However, an in depth knowledge of the interactions occurring in the system is still lacking. In this study, the interactions between sodium caseinate and high methoxyl pectin under acidification conditions were studied nondestructively and without dilution using transmission diffusing wave spectroscopy. Oil-in-water emulsions were prepared with 10% soybean oil and 0.5% sodium caseinate. Various concentrations of pectin (ranging from 0 to 0.2%) were added, and the emulsions were acidified with glucono-delta-lactone. With acidification, a "sol-gel" transition occurred and emulsions containing pectin were more stable at lower pH than those without pectin. Furthermore, the sol-gel transition of the mixtures was more sudden for control emulsions without pectin. While in control samples the final solidlike emulsion after gelation tended to be more inhomogeneous and more dissimilar to the starting emulsion, emulsions with pectin in solution gelled later under acidification. With a sufficient amount of pectin, the emulsions showed no aggregation and the destabilization pH varied depending on the amount of pectin present in the emulsions. At intermediate pH values (pH > 5.5), the emulsions displayed a decrease in particle size, more pronounced in samples containing pectin. The results collected using light scattering in concentrated systems, 10% (v/v) in our case, suggested that pectin stabilizes the emulsion oil droplets forming a network of oil droplets loosely connected by strands of pectin.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".