Effect of organic acids and NaCl on the rheological properties of dough prepared using Pembina and Harvest <scp>CWRS</scp> wheat cultivars
Bibliographic record
Abstract
Background and objectives The effect of organic acids (1.2 mmol/100 g flour: acetic, citric, lactic or fumaric acid) and NaCl content (0%, 1% and 2%, flour weight basis) on the rheological properties and stickiness of dough prepared using two CWRS wheat cultivars (Harvest and Pembina) was investigated as a means of understanding the interaction of salt and acid on dough viscoelastic behavior. Findings Overall higher salt levels made the doughs stiffer and less sticky as evident by |G*| and Jel increasing and dough stickiness decreasing as salt content increased. The addition of citric, acetic, and lactic acids caused an overall increase in |G*| and Jel, and a decrease in tan δ and Jmax, whereas fumaric acid had the inverse effect. Dough stickiness increased regardless of the type of acid added at all salt levels but more so when no salt was added to the dough. The effect of acid addition on dough rheology was cultivar dependent as evident by a small overall increase in |G*| with acid addition to Pembina and an overall decrease in |G*| when using Harvest. Conclusions The effect of cultivar, salt content and acid type was highly interdependent for the dough complex modulus, tan δ, maximum creep compliance, and relative elasticity along with dough stickiness. Significance and novelty This research provides fundamental knowledge on the effect of salt reduction and acidulants on dough rheology with a view to understanding how water incorporation in the gluten network affects attempts to reduce sodium content in the dough without impairing dough handling properties.
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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".