Unveiling the impact of durum wheat protein quantity and quality on textural properties and microstructure of cooked pasta
Bibliographic record
Abstract
Abstract Background and Objectives This study investigated the impact of protein quantity and gluten strength on mechanical properties and microstructure of cooked pasta using texture analyzer and scanning electron microscopy. Three Canadian durum varieties with wide range of gluten strength at four protein levels were selected. Findings Four distinct microstructures were characterized for cooked pasta. Pasta with firm texture exhibited compact and dense protein networks, while that with soft texture showed open, coarse, and heterogeneous structures. Protein content was the dominant factor in determining pasta firmness, the impact of gluten strength on pasta texture depends on protein content and cooking time. Greater impact from gluten strength was observed when pasta was overcooked. Conclusions The relative contribution of protein quantity and quality in cooked pasta texture depends upon the ranges of protein content, gluten strength, and degree of cooking. Significance and Novelty This study unveiled the impact of wheat protein content and gluten strength on textural and microstructural properties of pasta at three cooking times. Results generated from this study provide guidance for durum breeding programs, durum milling, and pasta processing industry in setting protein content and gluten strength targets to ensure pasta quality.
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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".