Swelling Index of Glutenin Test. II. Application in Prediction of Dough Properties and End‐Use Quality
Bibliographic record
Abstract
ABSTRACT Small‐scale tests, including SDS and Zeleny sedimentation, gel protein, insoluble glutenin content, and a newly developed method, the swelling index of glutenin (SIG), were compared with dough and gluten rheological parameters and end‐use quality parameters for 20 wheat cultivars or breeders lines. The SIG test is equal to or slightly better than the other small‐scale tests in prediction of dough strength. Quality parameters were divided into two groups according to associations with insoluble glutenin content and glutenin quality. The glutenin quality is defined as the glutenin swelling properties with short swelling time (≤5 min) that are contributed by soluble and insoluble glutenin content and their swelling properties. Parameters in the first group were mainly dependent on insoluble glutenin content and appeared to reflect gluten strength. Parameters in the second group were dependent not only on glutenin content, but also on glutenin quality. Small‐scale tests are best to predict quality parameters within the same group, but not those in the other group. The glutenin swelling curve, obtained with different swelling times, was correlated with mixograph or farinograph data. Dough development time in farinograph and mixing time in mixograph were strongly related to the swelling time of peak SIG value in the swelling curve (r = 0.92, r = 0.86, respectively, P < 0.001). Farinograph stability was significantly related to the time of swollen stage in swelling curves (r = 0.62, P < 0.01). Similar to mixograph or farinograph data, the glutenin swelling curves can be used to differentiate some strong cultivars that can not be differentiated by sedimentation, gel protein, and insoluble glutenin values.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".