Ultrasonic Investigation of the Effect of Vegetable Shortening and Mixing Time on the Mechanical Properties of Bread Dough
Bibliographic record
Abstract
Mixing is a critical stage in breadmaking since it controls gluten development and nucleation of gas bubbles in the dough. Bubbles affect the rheology of the dough and largely govern the quality of the final product. This study used ultrasound (at a frequency where it is sensitive to the presence of bubbles) to nondestructively examine dough properties as a function of mixing time in doughs prepared from strong red spring wheat flour with various amounts of shortening (0%, 2%, 4%, 8% flour weight basis). The doughs were mixed for various times at atmospheric pressure or under vacuum (to minimize bubble nucleation). Ultrasonic velocity and attenuation (nominally at 50 kHz) were measured in the dough, and dough density was measured independently from specific gravity determinations. Ultrasonic velocity decreased substantially as mixing time increased (and more bubbles were entrained) for all doughs mixed in air; for example, in doughs made without shortening, velocity decreased from 165 to 105 ms(-1), although superimposed on this overall decrease was a peak in velocity at optimum mixing time. Changes in attenuation coefficient due to the addition of shortening were evident in both air-mixed and vacuum-mixed doughs, suggesting that ultrasound was sensitive to changes in the properties of the dough matrix during dough development and to plasticization of the gluten polymers by the shortening. Due to its ability to probe the effect of mixing times and ingredients on dough properties, ultrasound has the potential to be deployed as an online quality control tool in the baking industry.
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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) | 0.000 | 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".