Relationship Between Gluten Structural Properties and Noodle Texture: Insights From Seven Wheat Varieties
Bibliographic record
Abstract
ABSTRACT A comprehensive understanding of how gluten properties affect noodle texture remains limited. This study examined the impact of gluten physicochemical and structural properties on noodle texture. Seven wheat varieties from China, France, Canada, and Australia were utilized. Results indicated that increased surface hydrophobicity and higher β‐sheet content determined based on the relative levels of each property among the tested varieties reduced gluten water retention, lowering noodle adhesiveness. Greater surface hydrophobicity also enhanced gluten thermal stability, improving noodle chewiness, hardness, and tensile properties. In contrast, higher α‐helix content increased solubility, while a greater proportion of high molecular weight gluten subunits (HMW‐GS) strengthened the gluten network, enhancing hardness and elasticity. Among the tested varieties, Australian durum wheat (AD) exhibited superior elasticity and balanced texture, with hardness (376.43 g), chewiness (267.13 g), adhesiveness (27.49), and resilience (0.815). These properties were linked to its high water‐holding capacity (3.03 g/g), solubility (0.3 mg/mL), and thermal stability (Td = 58.18°C). These findings clarify the role of gluten in noodle texture and establish protein‐based criteria for wheat selection in processing.
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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) | 0.000 | 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".