Prediction of emulsification behaviour of pea and faba bean protein concentrates and isolates from structure–functionality analysis
Bibliographic record
Abstract
The effects of different extraction methods on the structure-functionality and emulsification behaviour of pea and faba bean protein isolates, and concentrates were studied at pH 7 and 2, and a regression model was developed to predict emulsion characteristics based on protein properties. The concentrates produced by air classification had lower protein content but higher solubility in water compared to the isolates produced by isoelectric precipitation. The protein secondary structure did not show a consistent difference; however, much higher intrinsic fluorescence was observed for the soluble compared to the insoluble fractions. Interfacial tension of all faba proteins was lower than pea, while there was no significant difference between the concentrates and isolates. The higher protein content of the isolates was found to improve their water holding capacity. Canola oil (40 wt%)-in-water coarse emulsions, prepared with 2 wt% proteins and 0.25 wt% xanthan gum showed smaller particle size at pH 7 than pH 2, while the zeta potential, viscosity and gel strength were higher at pH 7. Emulsions stabilized with concentrates were better or comparable to the isolates in terms of particle size, zeta potential, and microstructure. The regression model predicted that an increase in solubility, intrinsic fluorescence, water and oil holding capacities are more favourable to decrease emulsion particle size, while an increase in solubility, intrinsic fluorescence would lead to higher emulsion destabilization. A decrease in interfacial tension was more favourable to lower destabilization. Emulsion viscosity was more dependent on water holding capacity compared to any other factor. Such models could be extremely beneficial for the food industry to modulate processing for the development of desired pulse protein ingredients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".