Implications of Blending Pulse and Wheat Flours on Rheology and Quality Characteristics of Baked Goods: A Review
Bibliographic record
Abstract
Bread is one of the most widely consumed foods in all regions of the world. Wheat flour being its principal ingredient is a cereal crop low in protein. The protein content of a whole grain of wheat is about 12-15% and is deficit in some essential amino acids, for example, lysine. Conversely, the protein and fibre contents of legume crops are between 20 and 35% and 15 and 35%, respectively, depending on the type and cultivar of the legume. The importance of protein-rich diets for the growth and development of body organs and tissues as well as the overall functionality of the body is significant. Thus, in the last two decades, there has been a greater interest in the studies on the utilization of legumes in bread production and how the incorporation impacts the quality characteristics of the bread and the breadmaking process. The addition of plant-based protein flours has been shown to produce an improved quality characteristic, especially the nutritional quality aspect of bread. The objective of this review is to synthesize and critically investigate the body of research on the impact of adding legume flours on the rheological attributes of dough and the quality and baking characteristics of bread.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.002 | 0.001 |
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".