Role of Gluten and Its Components in Determining Durum Semolina Dough Viscoelastic Properties
Bibliographic record
Abstract
ABSTRACT Gluten was isolated from three durum wheat cultivars with a range in strength. Gluten was further fractionated to yield gliadin, glutenin and high molecular weight (HMW) and low molecular weight (LMW) glutenin subunits (GS). The gluten and various fractions were used to enrich a base semolina. Enriched dough samples were prepared at a fixed protein content using a 2‐g micromixograph. Mixing strength increased with addition of gluten. Dynamic and creep compliance responses of doughs enriched with added gluten ranked in order according to the strength of the gluten source. Gliadin addition to dough resulted in weaker mixing curves. Gliadin was unable to form a network structure, having essentially no effect on dough compliance, but it did demonstrate its contribution to the viscous nature of dough (increased tan δ). Source of the gliadin made no difference in response of moduli or compliance. Addition of glutenin to the base semolina increased the overall dough strength properties. Glutenin source did influence both dynamic and compliance results, indicating there were qualitative differences in glutenin among the three cultivars. Enrichment with both HMW‐GS and LMW‐GS increased overall dough strength. Source of HMW‐GS did not affect compliance results; source of LMW‐GS, however, did have an effect. The LMW‐2 proteins strengthened dough to a greater extent than did LMW‐1. Mechanisms responsible for dough viscoelastic properties are described in terms of reversible physical cross‐links.
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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 | 0.001 | 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".