The Effect of Addition of High‐Melting Monoacylglycerol and Candelilla Wax on Pea and Faba Bean Protein Foam‐Templated Oleogelation
Bibliographic record
Abstract
Abstract Use of oleogels prepared from hydrocolloids has recently gained considerable attention as an alternative for trans and saturated fats. Lately, pulse proteins such as faba bean protein and pea protein have been successfully used to prepare oleogels using a foam‐templated approach. Although the pulse proteins are healthy oleogelators, high oil loss and low quality of cake baked using pulse protein‐stabilized oleogels due to its poor rheological properties challenged its use. The present study explored whether the addition of small amount of high‐melting monoglyceride (MAG) or candelilla wax (CW) can be used to improve the oil binding capacity, rheological properties, and baking qualities of pulse protein‐stabilized oleogels composed of 5% faba bean or pea protein concentrate with 0.25% xanthan gum foams. Different concentrations (0.5–3%) of MAG or CW were dissolved in canola oil at 80 °C, followed by addition into the freeze‐dried protein‐polysaccharide foams (pH 7) and quickly transferred to a refrigerator to facilitate the formation of oleogels. The crystallized additives were found to be reinforcing the protein foam network in the oleogels. With increase in concentration of CW and MAG, the oil binding capacity, firmness, cohesiveness, and storage moduli of the oleogels were increased. Oleogels with and without MAG or CW were then characterized and tested for their performance as a shortening replacer in model baked cakes. Findings showed improved textural properties of cake upon addition of MAG in the foam‐templated oleogels, however, compared to the shortening, negative effect on cake hardness and chewiness was still observed with the oleogels.
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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".