Effects of water, salt, and mixing on the rheological properties of bread dough at large and small deformations: A review
Bibliographic record
Abstract
Abstract Background and Objective A direct relationship between dough rheology and bread quality has been clearly revealed to show remarkable significance of studying dough rheological properties throughout breadmaking processes. This review paper has presented the effects of basic ingredients and mixing conditions on dough rheological properties at large and small deformations. Findings At both large and small deformations, dough rheological parameters have indicated that the variation in dough strength is due to a manipulation of ingredients and mixing conditions. The rheological properties of doughs over a wide range of formulations and processing conditions have been well characterized using the basic rheological models varying in the degree of complexity. Particularly, a power‐law gel model with only two parameters has a good ability for describing the strength and linear viscoelastic behavior of dough. Conclusions To devise strategies for achieving a desirable product quality, a deep understanding of the effects of ingredients and mixing conditions on dough rheology is in great need. Significance and Novelty Dough rheology insights not only benefit the bakery industry by screening suitable wheat flours for breadmaking, but also provide knowledge of how to improve dough handling properties during bakery manufacturing.
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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 | 0.003 |
| 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".