Ultrasonic Characterization of Unyeasted Bread Dough of Different Sodium Chloride Concentrations
Bibliographic record
Abstract
ABSTRACT Altering the properties of dough by reducing sodium chloride (NaCl) content affects aeration processes during mixing. The effect of NaCl concentration on the bubble size distribution (BSD) in unyeasted doughs was investigated by an ultrasonic transmission technique through analysis of frequency‐dependent ultrasonic phase velocity and attenuation coefficient. As NaCl concentration was decreased, the volume fraction of gas in the dough increased, resulting in a larger attenuation coefficient for the dough. From the peak in attenuation coefficient, estimates of the median radius and the width of the lognormal BSD in the dough were extracted, both of which were sensitive to the dough's NaCl concentration. As NaCl concentration was reduced, the bubble radius decreased and the width of the distribution increased, in accordance with expectations arising from changes in the dough's consistency. Over the course of 150 min, the radius increased (40–50%) and the width decreased (4–8%) for all dough formulations, consistent with changes in the BSD arising from disproportionation. These dynamic changes demonstrate that dough is an interesting soft material whose formulation can be manipulated to enable it to possess different BSDs; the diffusively driven evolution in these bubble sizes can be investigated noninvasively with ultrasound.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".