Pulse and Oilseed Protein-based Oil Structuring for Baking Application
Bibliographic record
Abstract
This chapter critically reviews and reports recent work on plant protein-based indirect oleogelation. So far, emulsion, foam, and hydrogel-based templates have been used for oil structuring by removal of the water via drying or solvent exchange followed by the addition of liquid oil. Typically, emulsion-templated oleogels have shown higher gel strength and better thixotropic recovery than foam-templated oleogels. Usually, the texture analyzer-measured hardness of protein-stabilized oleogel-based cakes was found to be higher than conventional shortening-based cakes. Only a handful of studies used sensory analysis, where a lot of variability was observed. When oleogels were prepared from faba protein and canola protein isolate-stabilized emulsions, heat-treatment to induce protein denaturation was found to improve the oleogel oil binding capacity and rheology. Between the two plant proteins, oleogels from canola protein were superior in quality than those from faba protein. The stability of the oleogels, however, did not affect the hardness of the cakes, and both the oleogel cakes were softer than the shortening-based cakes. The utilization of plant proteins for oil structuring is novel and promising, and it can provide beneficial effects of utilizing proteins and lowering saturated fat. However, more research is needed to understand the complex interaction of an oleogel with a food matrix during processing.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".