Open Access Effect of Extraction Methods and Wheat Cultivars on Gluten Functionality
Bibliographic record
Abstract
Strong (CWES) wheat flours was evaluated and compared. The extra-strong wheat cultivars had stronger dough properties and produced smaller bread loaves than AC Barrie. Modifications of a starch displacement gluten extraction method were evaluated. For optimal gluten formation and extraction, water to flour ratio of 0.87 % and dough mixing to 30 % after peak dough development were used. Water and cold ethanol were compared for their effectiveness in gluten extraction by evaluating gluten yield and functionality in a soft wheat flour blend. The ethanol method produced higher yields of gluten, but these gluten extracts had significantly lower protein contents than the respective glutens extracted with water. Farinograph analyses of soft wheat flour fortified with gluten extracts to 14.5 % protein content showed significant differ-ences in dough development time, stability and mixing tolerance index between water- and ethanol-extracted gluten ex-tracts; glutens extracted with ethanol had significantly stronger dough properties and also had higher 50PI:50PS gluten ra-tios. Whereas ethanol-extracted gluten decreased or had no effect on loaf volume, water-extracted gluten improved bread loaf volumes when added to soft wheat flour. The inherent differences in quality between CWRS and CWES flour was re-flected in the gluten extracted by water, but not in the gluten extracted by ethanol.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".