INVESTIGATION OF UNIAXIAL STRESS RELAXATION PARAMETERS TO CHARACTERIZE THE TEXTURE OF YELLOW ALKALINE NOODLES MADE FROM DURUM AND COMMON WHEATS
Bibliographic record
Abstract
ABSTRACT Durum wheat samples (three varieties), milled to yield straight‐grade and patent flours, were processed into YANs. CWHWS and CWRS flours, customarily employed to make noodles, were included for comparative purposes. Uniaxial stress relaxation parameters, %SR, K1 and K2, derived from Peleg's model, were determined for all cooked noodles. Analysis of variance indicated a significant durum sample effect (P < 0.0001) on all three parameters. Significant differences (P = 0.05) were observed among all three CWHWS parameters and the durum flour samples, but not for CWRS. Significant correlations were detected among the three stress relaxation parameters and empirical texture measurements: RTC, REC and MCS. Flour yield exhibited a significant effect (P = 0.05) on %SR, K1 and K2, which was not detected using the empirical texture measurements. The uniaxial stress relaxation test provides a complementary, discriminating method for YAN texture measurement. PRACTICAL APPLICATIONS This describes the use of uniaxial compression to characterize and discriminate Asian noodle quality texture parameters on the basis of rheological principles. It demonstrates the discriminatory power of three parameters to discern similar noodle flour sources. The technique and parameters are simple to calculate and are well correlated with traditional empirical texture measurements.
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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 | 0.001 | 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".