Structural Modifications of Gluten Proteins in Strong and Weak Wheat Dough During Mixing
Bibliographic record
Abstract
ABSTRACT The network‐forming attributes of gluten have been investigated for decades, but no study has comprehensively addressed the differences in gluten network evolution between strong and weak wheat types (hard and soft wheat). This study monitored changes in SDS protein extractability, SDS‐accessible thiols, protein surface hydrophobicity, molecular weight distribution, and secondary structural features of proteins during mixing to bring out the molecular determinants of protein network formation in hard and soft wheat dough. Soft wheat flour and dough exhibited greater protein extractability and more accessible thiols than hard wheat flour and dough. The addition of the thiol‐blocking agent N‐ethylmaleimide (NEM) resulted in similar results for protein extractability and accessible thiols in hard and soft wheat samples. Soft wheat dough had greater protein surface hydrophobicity than hard wheat and exhibited a larger decrease in surface hydrophobicity in the presence of NEM. Formation of high‐molecular‐weight (HMW) protein in soft wheat dough was primarily because of formation of disulfides among low‐molecular‐weight (LMW) proteins, as indicated by the absence of changes in protein distribution when NEM was present, whereas in hard wheat dough the LMW fraction formed disulfide interaction with the HMW fraction. Fourier transform infrared spectroscopy indicated formation of β‐sheets in dough from either wheat type at peak mixing torque. Formation of β‐sheets in soft wheat dough appears to be driven by hydrophobic interactions, whereas disulfide linkages stabilize secondary structure elements in hard wheat dough.
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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 | 0.000 | 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".