Improving wheat for bread and tortilla production by manipulating glutenin‐to‐gliadin ratio
Bibliographic record
Abstract
Abstract Gluten, starch, water soluble material, and glutenin‐rich and gliadin‐rich proteins were extracted from three Canadian wheat cultivars representing the Canada Western Red Spring (CWRS) (cv Roblin), Canada Western Extra Strong (CWES) (cv Glenlea) and Canada Prairie Spring (CPS) (cv AC Crystal) classes having glutenin‐to‐gliadin (Glu:Gli) ratios of 0.70, 0.75 and 0.85 respectively, all giving the same high‐molecular‐weight glutenin subunit score ( Glu‐1 score) of 10. The resulting fractions were reconstituted to produce 18 mixtures of flour components, representing all combinations of Glu:Gli ratio and protein content observed in the original three flours. Dough rheological properties and baking (bread and tortilla) performance were determined using small‐scale techniques. Within any of the cultivars, increasing the Glu:Gli ratio in a reconstituted dough system had significant effects on dough and end‐use properties, causing increases in mixograph development time (MDT), maximum resistance (Rmax), pan bread loaf volume, tortilla dough maximum resistance and cooked tortilla puncture force. The CWRS wheat Roblin, proved to be best suited for pan bread at higher protein content and higher Glu:Gli ratio, and also produced a high protein tortilla of large diameter at a Glu:Gli ratio of 0.70. The CPS flour, AC Crystal, was good for making tortillas at protein contents of 110–130 g kg −1 and at its original ratio of 0.85. The CWES wheat Glenlea, did not perform as well in bread or tortilla‐making but in its role as a blending wheat, altering the Glu:Gli ratio of Glenlea to 0.70 may have an advantage by lowering mixing time without compromising baking properties. Manipulating the Glu:Gli ratio may make a wheat cultivar suitable for a particular end‐product. For the Department of Agriculture and Agri‐Food, Government of Canada, © Minister of Public Works and Government Services Canada 2005. Published for SCI by John Wiley & Sons, Ltd.
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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".