Apparent Viscosity as a Marker of Wheat Germination and Predictor of Bread Quality and Starch Digestibility
Bibliographic record
Abstract
ABSTRACT Background and Objectives Wheat germination enhances its α‐amylase activity affecting the functional properties of flour and bread. Studies reported a variety of germination conditions, which does not allow comparison studies. This study evaluated the impact of germination on wheat flour functionality, breadmaking performance, and nutritional quality using peak viscosity after heating as a marker to control the degree of germination. Findings Wheat kernels were germinated until peak viscosity was reduced by 50% compared to control. A strong negative correlation was found between α‐amylase activity and peak viscosity ( r = −0.84, p < 0.0001), and a strong positive correlation between peak viscosity and falling number ( r = 0.98, p < 0.0001), validating the use of peak viscosity as a predictor of enzymatic starch degradation. Bread from fixed time germinated flour showed varying reductions in hardness and chewiness, whereas bread from adjusted germinated flour demonstrated more consistent levels. Conclusions Viscosity‐adjusted germination effectively controls enzymatic activity and influences both dough handling and bread texture. It also leads to slower starch hydrolysis. Significance and Novelty Peak viscosity is proposed as a practical and quantitative marker of wheat germination level. By controlling enzymatic activity through viscosity adjustment, it is possible to optimize bread quality and nutritional outcomes.
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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 | 0.001 | 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 | 0.000 |
| 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".