Application of Barley‐ and Lentil‐Protein Concentrates in the Production of Protein‐Enriched Doughnuts
Bibliographic record
Abstract
Abstract Barley and lentil proteins have good emulsifying and foaming capacity, which make them suitable ingredients in many food formulations. The aim of this research was to develop protein‐enhanced foods based on barley and lentil proteins using doughnut as a food model. In enriched doughnut formulations, barley proteins were used to substitute for 20% of the wheat flour (doughnuts labeled as barley doughnut, barley/lentil doughnut [BLD], and vegan doughnut [VD]) and lentil proteins were used as an egg replacer (in BLD and VD doughnuts). A VD formulated with barley and lentil proteins to replace egg white protein and milk was also tested. The physicochemical parameters, proximate composition, and sensory qualities of the prepared doughnuts were studied and compared with the control doughnut (CD) containing egg white and milk. The protein‐enriched doughnuts showed better cooking characteristics with reduced cooking loss and diameter reduction compared to the control. In addition, they had a higher protein and β‐glucan content. During the storage‐stability test, the doughnuts containing lentil and barley proteins showed higher values for hardness, chewiness, and gumminess compared to the control. However, the sensory scores for appearance, flavor, and overall liking were rated more favorable toward the CD. It is also noticed that the fat content and caloric values increased for some protein‐enriched doughnut formulations. Considering the consumers’ demand of high‐protein convenient snack foods, the doughnuts prepared with barley and lentil proteins may have potential for commercialization with some improvements.
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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) | 0.000 | 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".