Velocity and attenuation analysis methods for characterizing the properties of wheat flour noodle dough
Bibliographic record
Abstract
Abstract Background and objectives Noodles are a traditional staple of the Asian diet, and ensuring quality during production is important for both producers and consumers. Ultrasound is sensitive to the mechanical properties of dough that relate to product texture and is therefore a promising quality assessment tool. For optimal results, it is important to assess the merits of different ultrasonic analysis methods. Findings Mechanical properties of raw Asian noodle dough made from two red spring wheat varieties were measured by a low‐frequency ultrasonic transmission technique. Entire transmitted ultrasonic pulses, having passed through disks of the noodle dough, were analyzed using fast Fourier transform (FFT) techniques, enabling the transmitted phase and amplitude as a function of frequency to be compared for samples of different thickness. This approach allows noodles made from different wheat varieties to be discriminated through ultrasonic velocity and attenuation measurements. Conclusions The frequency dependence of both velocity and attenuation coefficient, which was revealed the FFT method, points to the presence of gas bubbles, which even in small amounts can influence texture and hence noodle quality. Significance and novelty In addition to providing insights on properties that influence noodle quality, the frequency dependence of velocity and attenuation found by the FFT analysis can explain differences reported by different analysis techniques.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".