Asparagine and dough quality: Gluten strength relationships in hard red spring wheat
Bibliographic record
Abstract
Abstract Background and objectives The presence of acrylamide, a probable carcinogen, is a global concern for the baking industry. The reduction of free asparagine in wheat is an effective strategy to mitigate acrylamide formation. However, field‐based strategies for this purpose also affect gluten strength and bread quality. Therefore, we investigated the relationship between wheat free asparagine concentration, gluten strength and whole wheat bread quality for an extensive set of hard red spring wheat samples. Findings Gluten strength parameters negatively correlated to wheat free asparagine concentration and showed strong inverse correlations to wheat free asparagine concentration per unit mass of protein. Reducing free asparagine concentration in the wheat did not affect the quality of the bread. Conclusions In efforts to deliver wheat grains with a low acrylamide‐formation potential, wheat producers can apply strategies, for example, variety selection, that will reduce free asparagine without worries that gluten strength and bread quality will be impaired. Significance and novelty Safety concerns regarding free asparagine in wheat have elicited much attention from cereal scientists and the breadmaking industry. However, the effects of reducing free asparagine on gluten strength have not been systematically studied in the literature. This comprehensive study shows that there is an inverse relationship between gluten strength and wheat free asparagine concentration.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".