Influence of salt, thermal treatment and protein physical modification on the development of faba bean protein-stabilized elastic emulsion gel
Bibliographic record
Abstract
Protein-stabilized emulsion gels are structured soft materials formed by an aggregated network of protein-coated oil droplets holding the aqueous phase immobile. In this study, faba bean protein was physically modified by thermal and high-pressure homogenization before being used to prepare highly stable 30 wt% canola oil-in-water emulsions. The stability and gelation behaviour of the emulsions were characterized by droplet size, charge, small and large deformation rheology and freeze-thaw stability. The effect of protein modification was negligible on droplet size but significant in terms of large deformation rheology and freeze-thaw stability. Heat treatment (90 °C, 30 min) and salt addition (0–3 wt%) resulted in droplet aggregation, converting viscous emulsions into strong, viscoelastic, self-supporting gels. Large deformation rheology revealed that heat treatment significantly increased fracture stress but reduced fracture strain, indicating brittleness in the structure. The emulsions were freeze/thaw stable without any droplet destabilization, except that the unsalted emulsions showed some oiling-off. The microstructure of the emulsion gels revealed an extensive droplet and protein network, which was enhanced by protein modification and salt addition. Investigation of gelation mechanism revealed that the hydrophobic interaction among the interfacial proteins around the oil droplets was the most dominant, followed by hydrogen bonds and disulphide bonds in the heated protein emulsion gels. The stable, self-supporting, strong elastic emulsion gels containing only 30 wt% oil and stabilized using faba protein, created with the selective addition of salt and heat treatment of the emulsions, can be utilized in the development of sustainable, plant-based fat replacers in food formulations.
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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".