Application of Ultrasound to the Evaluation of Rheological Properties of Raw <scp>A</scp>sian Noodles Fortified with Barley <i>β</i>‐Glucan
Bibliographic record
Abstract
Abstract An ultrasonic technique (1 MHz) was employed to investigate the capability of ultrasound to evaluate barley β ‐glucan ( BBG ) supplementation (0%, 2.5% and 5%) on the mechanical properties of raw noodles. The noodles were subjected to a 20% strain using a texture analyzer in which a custom holder for ultrasonic transducers enabled stress relaxation and ultrasonic propagation to be observed over 300 s. Ultrasonic velocity and attenuation increased and decreased, respectively, with an increase in noodle BBG content. Similarly, the longitudinal storage modulus M ′ increased, while the long‐time values of the longitudinal loss modulus M ″ decreased, as the BBG content was increased. The stress relaxation parameter % SR 20s decreased significantly, while P eleg's K 1 and K 2 values increased with increasing BBG content, supporting the ultrasonic findings that the noodles displayed an enhanced resistance to deformation with an increase of BBG content. The ultrasonic technique discerned changes in the mechanical behavior of functional food products. Practical Applications This research describes the use of a relatively inexpensive ultrasonic technique to discriminate and quantify desirable improvements in raw A sian white salted noodles on the basis of their fundamental rheological parameters. The test is rapid and allows the calculation of multiple parameters to highlight the texture benefits of adding BBG to noodle flour.
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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 | 0.000 |
| 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".