Role of <scp>NaCl</scp> level on the handling and water mobility in dough prepared from four wheat cultivars
Bibliographic record
Abstract
Abstract In bread, NaCl plays a number of roles including improving flavor, functionality, dough handling, and prevention of sticky dough. Its reduction can create significant processing challenges. As such, the dough handling properties for four wheat cultivars (Pembina, Roblin, McKenzie, and Harvest) were investigated as a function of NaCl (0–4%) level. In terms of dough rheology, both cultivar and NaCl level were significant factors. The maximum deformation (Jmax) in the dough decreased with increasing NaCl levels, indicating that the gluten network became stronger so that it was able to resist the imposed stress. For extensibility, increasing the levels of NaCl resulted in increased resistance to extension for all cultivars. Dough stickiness was shown to be both cultivar and salt level dependent, with weaker cultivars showing higher stickiness. Findings for water association indicated that with the addition of NaCl there was less free water among the different cultivars and an increase in the water associated with the starch‐fraction. Dough morphology measurements supported rheology trends; the stronger dough producing cultivars created more elongated protein polymers with a unidirectional network whereas the weaker cultivars created porous multidirectional networks. Overall, Pembina and Roblin formed stronger gluten networks than McKenzie and Harvest, however, the effect of NaCl level was shown to be cultivar dependent. Findings indicate that careful cultivar selection will help mitigate challenges in dough handling within a reduced NaCl environment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".