Expanding the application of germinated wheat by examining the impact of varying alpha-amylase levels from grain to bread
Bibliographic record
Abstract
Controlled germination is recognized for its potential to enhance both the nutritional profile and functional properties of grains, but there is limited information about the level of wheat germination that promotes functional changes without losing breadmaking potential. This research aims to analyze the physicochemical changes in wheat during germination for 36 h, focusing on evaluating kernel changes, the flour breadmaking functionality, and bread characteristics. Pasting properties progressively decreased as the germination progressed, and apparent viscosity was barely detected after 36 h germination. Initial decline in gluten index was observed at 24 and 36 h germination, but gluten kept its aggregation capabilities. Optimal germination periods of 6–18 h significantly improved flour functionality, evidenced by increased Gluten Performance Index, gluten index, and enhanced dough mixing properties. Further, mini-breads, developed after optimizing breadmaking conditions, displayed increased 2D areas in 24 and 36 h and lower crumb hardness in 24 and 36 h of germinated bread compared to those obtained with sound wheat flour. Significant correlations were found among alpha-amylase activity, Falling number, total and damaged starch content, RVA parameters, gelatinization enthalpy, and breadcrumb texture parameters. Developing mini bread using different levels of germinated flour demonstrates its viability for breadmaking offering a promising innovation within the whole-grain food industry.
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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".