Dynamic Viscoelastic and Tensile Properties of Gluten and Glutenin Gels of Common Wheats of Different Strength
Bibliographic record
Abstract
Dynamic viscoelastic properties at 25 degrees C of gluten and glutenin gels were obtained from Canadian common wheats of different strengths. The relaxation spectra showed a maximum intensity at a characteristic relaxation time (tau). The relaxation modulus associated with this maximum was taken as the strength of the glutenin or gluten gel transient network (G(tau)). The ratio of G(tau) for glutenin and gluten gels from the same cultivar ranged from 5.6 for an extra strong cultivar to 51.1 for a soft wheat. This gives indirect evidence that the gliadin fraction weakens the glutenin gel network more in weaker cultivars. In addition, the fact that both glutenin and gluten gels showed extensive stress relaxation coupled with the fact that addition of l-cysteine to a gluten gel eliminated the network structure at 25 degrees C and resulted in a power law stress relaxation spectrum suggests that the transient network in gluten is a reversible network. This power law relaxation pattern was not seen here for an entangled polymer melt (poly(dimethylsiloxane)). It was also found here that the viscosity of the gluten gel (G(tau) x tau) trended best with the tensile stress build-up in a uniaxial tensile test of gluten gels. Together, these results indicate that both network strength and relaxation times should be considered in characterizing the linear viscoelastic properties of hydrated cereal proteins.
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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 | 0.001 | 0.001 |
| 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".