Stability and rheology of canola protein isolate stabilized concentrated oil-in-water emulsions
Bibliographic record
Abstract
Abstract Salt-extracted canola protein isolate (CPI) from a cold-pressed meal was used (1–4 wt%) to develop concentrated 50% canola oil-in-water emulsions (pH 7) using a high-pressure homogenizer and the effect of various environmental factors on emulsion stability and rheology was investigated. As CPI concentration increased, droplet size decreased from 16.4 to 3.8 μm while the droplet charge remained constant at around −11 mV. All emulsions flocculated over 30 days but exhibited exceptional resistance to coalescence. Storage moduli of all emulsions were higher than the loss moduli at all CPI concentrations, suggesting a gel-like structure. Emulsion stability was also investigated by adding vinegar (10 wt%, pH 3.7) or salt (1 wt%) or a mixture of both and heating at 80 °C. The addition of either salt or vinegar reduced the viscosity and gel strength of emulsions, compared with the non-treated emulsions. No significant change in microstructure was observed with the addition of either vinegar or salt, but in the presence of both, the droplets were extensively aggregated, leading to a non-flowing strong gel structure with significantly higher viscosity and gel strength. Salt addition led to charge screening, but the droplets remained less flocculated due to steric repulsion, while with acid, higher charge prevented aggregation. When both salt and acid were present, lower charge and change in protein conformation led to extensive droplet aggregation. Heat treatment led to an approximately ten-times increase in gel strength, which could be attributed to CPI thermal denaturation leading to droplet and protein aggregation. These findings may extend the application of CPI in viscoelastic foods such as salad dressing.
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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".