Heat-Induced Changes Occurring in Oil/Water Emulsions Stabilized by Soy Glycinin and β-Conglycinin
Bibliographic record
Abstract
Glycinin and β-conglycinin are the two major proteins in soy protein isolate, and their emulsifying behavior was the subject of this study. These proteins form a thin layer of 30-40 nm when adsorbed at the interface. Microcalorimetric experiments showed that the thermal transitions of these proteins in the emulsion were very similar to those of the proteins in solution. The results also suggested that molecular rearrangements occurred during adsorption of β-conglycinin, as an endothermic transition peak appeared at high temperature when this protein was present at the interface. In general, β-conglycinin exhibited greater emulsifying activity than glycinin, confirming previous reports. Heating at 95 °C for 15 min caused a decrease in solubility of glycinin, and interactions between the oil droplets, with an increase in the apparent viscosity, shear thinning behavior, and droplet particle size distribution of the emulsions. While, similar behavior was noted in b-conglycinin after heating at both 75 and 95 °C. Furthermore, the order of processing affected the subunits' composition at the interface. Heating the solution before emulsification caused a higher protein load at the interface and with all of the subunits present. On the other hand, when heating was carried out after homogenization, the basic glycinin polypeptide and the β subunit of β-conglycinin were absent from the interface, suggesting that heat-induced complexes between these subunits formed and remained soluble in the unadsorbed phase.
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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 | 0.001 |
| 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".