A Review of the Utilization of Canola Protein as an Emulsifier in the Development of Food Emulsions
Bibliographic record
Abstract
Canola is the second-largest cultivated oilseed crop in the world and produces meal consisting of about 35-40% proteins. Despite this, less than 1% of the global plant-based protein market is taken up by canola protein. The reason behind such underutilization of canola protein and its rapeseed counterpart could be the harsh conditions of the industrial oil extraction process, the dark colour of the meal, the presence of various antinutrients, the variability in the protein composition based on the source, and the different properties of the two major protein components. Although academic research has shown immense potential for the use of canola protein and its rapeseed counterpart in emulsion development and stabilization, there is still a vast knowledge gap in efficiently utilizing canola proteins as an effective emulsifier in the development of various emulsion-based foods and beverages. In this context, this review paper summarizes the last 15 years of research on canola and rapeseed proteins as food emulsifiers. It discusses the protein extraction methods, modifications made to improve emulsification, emulsion composition, preparation protocols, and emulsion stability results. The need for further improvement in the scope of the research and reducing the knowledge gap is also highlighted, which could be useful for the food industry to rationally select canola proteins and optimize the processing parameters to obtain products with desirable attributes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".