Structuring canola oil with canola proteins
Bibliographic record
Abstract
Oleogelation is an approach to structure liquid oil without using saturated or trans-fat for improved health benefits. With the official ban of the partially hydrogenated oil in the United States and Canada in 2018, this alternative oil structuring approach have gained increased interest over the years. The overall goal of the research work was to develop emulsion-templated oleogel from canola protein-stabilized concentrated oil-in-water (O/W) emulsions. Canola protein isolate (CPI) (1-4 wt%), extracted from cold-pressed canola meal, was used to stabilize 50 wt% O/W emulsions using high-pressure homogenization, and the effect of different environmental factors on the emulsions stability and rheology was studied. The emulsions were stable to coalescence with the addition of 1 wt% salt, 10 wt% vinegar, both salt and vinegar and heat treatment (80 °C for 30 min). All emulsions exhibited higher storage (G′) than loss moduli (G″) before crossover in the strain-dependent oscillatory rheological analysis, suggesting gel-like behaviour. In the presence of salt or vinegar, gel strength decreased due to the salt-soluble nature of CPI and higher droplet charge in the presence of vinegar. With the addition of both salt and vinegar, the droplets aggregated and formed a strong gel due to the charge screening effect from salt and increased surface hydrophobicity in acidic pH. The heated emulsions exhibited about ten times higher in gel strength than the unheated emulsions, which was attributed to the protein denaturation leading to extensive droplets and protein aggregation. Subsequently, 1 and 4 wt% CPI-stabilized emulsions (unheated and heated) were vacuum dried and sheared to form oleogels. In general, the heated emulsion (HE) oleogel showed a higher gel strength and oil binding capacity than the unheated emulsions (UE) oleogel. Among all, the 4 wt% CPI HE oleogels showed the highest gel strength, firmness, stickiness and oil binding capacity, indicating better retained structure. Hence, the 4 wt% CPI oleogels was selected as shortening replacer in cake baking. Oleogel cakes were softer, and their specific volume was also significantly higher than the shortening cake. It was proposed that the protein-stabilized continuous air channels, observed in the HE oleogel cakes, contributed to its higher cake volume leading to lower hardness than the shortening cake. Overall, this research showed that canola protein isolates from cold-pressed meal can be used as a highly efficient stabilizer of concentrated O/W emulsion, which can be used in both liquid and gelled food applications. It also successfully demonstrated the application of canola protein isolate in oil structuring by forming emulsion-templated oleogels as a successful replacer of conventional highly saturated fat in cake baking.
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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".