Interactions between Whey Protein Isolate and Soy Protein Fractions at Oil–Water Interfaces: Effects of Heat and Concentration of Protein in the Aqueous Phase
Bibliographic record
Abstract
ABSTRACT: The 2 main storage proteins of soy—glycinin (11S) and β‐conglycinin (7S)—exhibit unique behaviors during processing, such as gelling, emulsifying, or foaming. The objective of this work was to observe the interactions between soy protein isolates enriched in 7S or 11S and whey protein isolate (WPI) in oil–water emulsion systems. Soy oil emulsion droplets were stabilized by either soy proteins (7S or 11S rich fractions) or whey proteins, and then whey proteins or soy proteins were added to the aqueous phase. Although the emulsifying behavior of these proteins has been studied separately, the effect of the presence of mixed protein systems at interfaces on the bulk properties of the emulsions has yet to be characterized. The particle size distribution and viscosity of the emulsions were measured before and after heating at 80 and 90 °C for 10 min. In addition, SDS‐PAGE electrophoresis was carried out to determine if protein adsorption or exchanges at the interface occurred after heating. When WPI was added to soy protein emulsions, gelling occurred with heat treatment at WPI concentrations >2.5%. In addition, whey proteins were found adsorbed at the oil–water interface together with 7S or 11S proteins. When 7S or 11S fractions were added to WPI‐stabilized emulsions, no gelation occurred at concentrations up to 2.5% soy protein. In this case also, 7S or 11S formed complexes at the interface with whey proteins during heating.
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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 | 0.001 | 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 | 0.001 |
| 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".