Quality characteristics of angel food cake and muffin using lentil protein as egg/milk replacer
Bibliographic record
Abstract
Summary Replacement of animal proteins could be interesting for the food industry because it allows long‐term cost savings, among other reasons. Replacing egg/milk protein (50–100 wt%) by lentil protein ( LP ) was evaluated on angel cake/muffin quality. The replacement did not significantly affect final product volume, neither the muffins nor the angel food cakes. LP did not affect dough formation and contributed to hold crumb structure building an entangled network in both cake products. In addition, angel cakes and muffins containing LP had significantly lower baking loss than the control. Inferior quality for angel cakes and muffins containing LP was observed regarding hardness and chewiness that increased upon storage, compared to the control. For sensory evaluation in angel cakes, appearance of LP formulations showed lower scores than the control, likely due to the change of crumb colour. Other attributes were not significantly impacted by LP presence. For muffins, M‐100‐ LPC formulation showed significant differences with the control for most of the attributes, except appearance and flavour. Indeed, consumers preferred muffins with 100% egg/milk protein replacement, which received higher acceptability scores than control. They also appreciated the ‘nutty’ flavour and moisture of angel cake with 50% egg protein replacement. This research suggests that lentil protein can totally or partially substitute egg/milk protein as foam and emulsion stabiliser in cakes, producing products with satisfactory quality.
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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".