Use of ultrasound to determine the effects of sheeting work input and barley β-glucan addition on mechanical properties of Asian wheat noodles
Bibliographic record
Abstract
Empirical tests developed for an objective evaluation of noodle texture as well as sensory methods provide no information about the rheological parameters responsible for differences in the texture of Asian noodles. Therefore, there is a need for a technique to evaluate the rheological properties of Asian noodles that is fast, easy and inexpensive. Promising results from ultrasonic assessments of the rheological properties of wheat flour doughs and preliminary noodle studies were an encouragement to use ultrasound to address such need in this thesis. Using ultrasound at a relatively high frequency (1.4 MHz), the rheological properties of raw noodles made with three Canadian wheat varieties at various barley β-glucan (BBG) contents, sheeting work input levels and salt to kansui ratios (formulas) were evaluated. Conventional rheological, i.e., stress relaxation (SR) and uniaxial extension (Kieffer), measurements were employed for comparison purposes. The capability of ultrasound for evaluating the effects of BBG addition and sheeting work input on the rheological properties of raw wheat noodles of this study was confirmed. A greater sensitivity of ultrasound to microscopic changes in noodle dough structure, compared to SR and Kieffer tests, was reflected in differences between the results of ultrasound and those of the SR and Kieffer tests with respect to the influences of flour variety and formula on noodle rheology. Investigations of the density of raw noodles showed that air bubbles were present in the noodle dough matrix. A simple, fast and low-cost method, based on the measurement of noodle dough dimensions during the sheeting process, was proposed for a quick evaluation of noodle dough rheology. The results of the proposed method were in agreement with those of conventional rheological measurements on the effects of flour variety and sheeting work input on noodle texture. The time-dependent effect of kansui on noodle dough structure was confirmed by comparing the results of this new method with those of SR and Kieffer tests with respect to the effect of formula on noodle rheology. Further investigations are proposed to establish this method for quick assessment of Asian wheat noodle rheology.
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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) | 0.000 | 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".