The effect of addition of high-melting monoacylglycerol and candelilla wax on pea and faba bean protein foam-templated oleogelation
Bibliographic record
Abstract
Oleogels prepared from hydrocolloids have recently gained a lot of attention as an alternative for trans and saturated fats. Previously we have demonstrated that the freeze-dried foams prepared using a mixture of 5% faba bean or pea protein concentrates with 0.25% xanthan gum at pH 7 and 9 can hold canola oil 30-40 times their weights (Mohanan, Tang, Nickerson and Ghosh, 2020). However, the oleogels suffered from high oil loss, about 30% oil leaked, which negatively affected the rheological properties of the oleogels. The functionality of the cake baked using the oleogels was poorer compared to a shortening baked cake. The present study explored the addition of a small amount of high-melting monoacylglycerol (MAG) and candelilla wax (CW) on reducing oil loss, improving rheological properties and baking qualities of pulse protein-stabilized oleogels. Different concentrations (0.5-3%) of MAG or CW were dissolved in canola oil at 80 ºC. The hot oil was then added into the freeze-dried protein-polysaccharide foams (pH 7) and quickly transferred to a refrigerator. The crystallized additives reinforced the oleogel network, thereby reducing oil loss while increasing the firmness, cohesiveness, and storage modulus. When model cakes were baked with the oleogels, significant improvement in textural properties was observed with the addition of MAG in the foam-templated oleogels. However, in comparison with shortening-based cakes, oleogel-based cakes still showed a negative effect on hardness, chewiness and cohesiveness.
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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".