Investigating the effects of formulation and processing conditions on the mechanical properties of wheat flour noodle dough
Bibliographic record
Abstract
Characterization of the mechanical properties of soft food materials is crucial in the food industry, both for process design and for quality enhancement purposes. The goal of this project is to develop a non-contact on-line quality control technique for use in processing of sheeted products such as Asian noodles. In the case of the Asian noodle industry, composition and work input during the sheeting process are important parameters that influence the mechanical properties of the dough, and as a consequence, final product quality. An accurate and fast determination of noodle properties and how they are influenced by formulation and processing parameters will certainly improve quality control during production. To address this need, we are conducting a full study of noodle dough with ultrasonic techniques. A non-contact ultrasonic technique in transmission is used to assess the mechanical properties of noodle dough sheets and has been evaluated in pilot-plant trials, showing its feasibility for real-time quality control. Additionally, for a better understanding of the material properties, including how both bubbles and the dough matrix are affected by sheeting, contact longitudinal experiments over a wide range of frequencies (0.5 to 12 MHz), along with ultrasonic shear experiments, have been performed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".