High Protein Functional Breads for Sustainable Nutrition: A Futuristic Review
Bibliographic record
Abstract
Bread is consumed by almost every household worldwide as a dietary staple. Most commercial bread products are made with refined wheat flour and have an incomplete nutritional profile. Refined wheat bread is comparatively lower in protein with an unbalanced amino acid profile and is low in fibre, vitamins, minerals, and phytochemicals. The enrichment of bread to increase nutritional quality and functionality while preserving sensory properties has been a point of interest for decades. Legumes and pulses are nutrient-dense plant ingredients capable of increasing and balancing the nutritional value of bread, especially the protein quality. The review aims to explore possible legumes and pulses for bread enrichment and recent developments in the study area, balancing the amino acid profile of bread, the behaviour of legume anti-nutritional factors in bread making, enhancing protein metabolism, associated challenges, and future directions. The Enrichment of bread with legumes and pulses will ensure a high protein intake, a balanced amino acid profile, and additional vitamin, mineral, and phytochemical content compared to refined wheat bread. The development and commercialization of enriched functional bread products will benefit a vast population, especially in developing countries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".