Gluten Structural Evolution During Pasta Processing of Refined and Whole Wheat Pasta from Hard White Winter Wheat: The Influence of Mixing, Drying, and Cooking
Bibliographic record
Abstract
An attempt was made to evaluate gluten structural changes in refined and whole wheat pasta from hard white winter wheat to elucidate the impact of whole wheat components on the formation and structure of the gluten network in pasta. Attenuated total reflectance–FTIR spectroscopy was used to track gluten secondary structure through most of the major steps in pasta processing: raw material, mixing, drying, and cooking. Protein solubility, accessible thiols, and SDS‐PAGE data were also collected to provide additional information on the nature of protein interactions and network composition. Few secondary structural differences were observed between refined and whole wheat flours from hard white wheat. However, mixing induced a significant shift to β‐sheet structures in refined dough that was not equally matched by whole wheat dough. Drying under both high temperature, short time (HT) and low temperature, long time (LT) conditions resulted in a reversion to structural distributions similar to those for flour in both pastas. However, greater protein denaturation in HT samples was indicated by lower protein solubility also in the presence of denaturants and disulfide reducing agents. Cooking generated a substantial increase in β‐sheet structures for both pasta systems. This structure was greatest in refined and LT samples. Thiol accessibility data indicate the presence of a highly aggregated, compact gluten network in refined pasta, mostly driven by hydrophobic association. Conversely, the network in whole wheat pasta was more loosely associated and dependent on disulfide bonding, both of which fit well with the secondary structural data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".