The entrainment and evolution of gas bubbles in bread dough—A review
Bibliographic record
Abstract
Abstract Background and Objective For control of bread quality to achieve high loaf volume and uniform crumb structure, gas bubble dynamics in dough needs to be better understood throughout different breadmaking processes. The objective of this review was to establish a solid theoretical basis on how flour type, water and salt content, and mixing conditions affected the incorporation, evolution, and stabilization of gas bubbles in a dough. Findings Bubble dynamics including entrainment, disentrainment, break‐up, disproportionation, growth, and coalescence were outlined and their effects on the gas phase of the dough were assessed. The application, advantages, and disadvantages of microscopy, magnetic resonance imaging (MRI) and X‐ray microtomography techniques for qualitatively or quantitatively characterizing the void fraction and bubble size distribution (BSD) in the dough at various stages of the breadmaking process have been discussed. Conclusions Since the BSD evolution in bread dough is associated with the quality of the resultant products, to devise strategies for improving the product quality, dough formulation, and mixing conditions need to be considered. Significance and Novelty Due to the obvious challenges of monitoring the fast evolution of BSD in yeasted dough, future research needs to focus on the effects of yeast activity on dough's BSD.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".