Effect of enzymatic crosslinking on the handling properties of dough as a function of NaCl levels for CWRS varieties, Pembina and Harvest
Bibliographic record
Abstract
The effects of transglutaminase (TG) and glucose oxidase (GO) on the handling properties of model bread dough were examined at both normal (2% wt. by flour) and reduced (1% wt.) sodium chloride (NaCl) levels using two Canada Western Red Spring (CWRS) cultivars; Pembina and Harvest. The reduction of NaCl level had negative effects on dough rheology and stickiness, however, the inclusion of GO (0.001 and 0.01% by flour weight) or TG (only at the 0.5% by flour weight inclusion) was able to improve dough strength and reduce stickiness. GO appeared to be more effective than TG (at 0.01%) at equivalent concentrations for improving dough-handling properties. Flour cultivar had significant effects; Harvest flour (weaker) was more impacted by salt reduction and enzyme inclusion compared to Pembina flour (stronger). Crosslinking assays showed significant differences in glutenin macropolymer (GMP) content in dough prepared with GO, and dough prepared with different flours. Additionally, significantly fewer free thiol groups were found in dough produced with GO compared to dough without any enzymes and those with TG. GO appears to have potential for use as a bread improver to reduce stickiness and improve the strength of bread dough produced at lower salt concentrations, especially for dough prepared with weaker flour cultivars.
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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 | 0.001 |
| 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".