Effect of chemical oxidizers and enzymatic treatments on the baking quality of doughs formulated with five Canadian spring wheat cultivars
Bibliographic record
Abstract
For many years, the baking industry has been using chemical improvers as a way for compensating for flour quality variation due to growing conditions or wheat cultivar. However, the replacement of chemical dough improvers with natural ingredients or processing aids (i.e. enzymes) allows for the production of 'cleaner label' products. In the present research, dough and bread properties (mixing time, oven rise, loaf volume, crumb firmness and C-cell parameters) were analysed as a function of wheat cultivar (Glenn, Harvest, Lillian, CDC Plentiful and Stettler), additive-type (ascorbic acid, azodicarbonamide, glucose oxidase and fungal xylanase) and concentration. Overall, the cultivar Glenn appeared to have improved baking performance relative to the other cultivars, regardless of the additive and additive concentration. On the other hand, Stettler showed poorer baking quality and performance even with the addition of oxidizers and enzymes in relation to the control. The concentration of additive was found to have little or no effect on improving baking properties within each cultivar. Enzymes had similar or better performance than oxidizers in most cases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".