Canola protein thermal denaturation improved emulsion-templated oleogelation and its cake-baking application
Bibliographic record
Abstract
The stability and viscoelasticity of an oil-in-water emulsion formed with canola proteins could be significantly improved by heat-induced protein thermal denaturation followed by aggregation at the oil droplet surface. This phenomenon was used to develop emulsion-templated oleogels with improved rheology and used in cake baking. Canola oil (50 wt%)-in-water emulsions stabilized by 1 and 4 wt% canola protein isolates (CPI), prepared by high-pressure homogenization, were dried at 60 °C in a vacuum oven followed by shearing to create the oleogels. Before drying, the emulsions were heated (90 °C for 30 min) to induce protein denaturation. The oleogel from 4 wt% CPI heated emulsions (HE) exhibited the lowest oil loss, highest gel strength, firmness and stickiness compared to all other oleogels. Cake batter prepared with shortening showed the lowest specific gravity, highest viscosity and storage modulus compared to CPI oleogels. Confocal micrographs of shortening cake batters showed smaller air bubbles entrapped in the continuous fat phase. In comparison, the oleogel cake batters showed dispersion of larger air bubbles, oil droplets, and protein aggregates. The oleogel cake showed a darker colour compared to the shortening cake due to the dark colour of CPI. Interestingly, oleogel cakes showed lower hardness, higher cohesiveness and springiness than the shortening cake, which was attributed to the higher cake volume of the former due to the formation of larger air channels stabilized by canola proteins. In conclusion, CPI stabilized emulsion-templated oleogels could be used as a potential shortening replacer in cake and other baking applications.
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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 | 0.000 |
| 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".