Dough Microextensibility Method Using a 2‐g Mixograph and a Texture Analyzer
Bibliographic record
Abstract
ABSTRACT Development of a small‐scale method to measure dough extensibility, using a 2‐g mixograph and the TA.XT2 texture analyzer (TA) equipped with Kieffer rig, suitable for early‐generation wheat quality screening is presented. Three hook speeds 3.3, 7.0, and 10.0 mm/sec were tested on the TA. Only at the lower hook speed of 3.3 mm/sec were wheats, varying in quality, clearly differentiated. The ability to differentiate between wheats using the TA was compared with the Brabender Extensigraph. The sample ranking based on the resistance to extension (Rmax) from the TA at a hook speed of 10.0 mm/sec correlated highly (r = 0.99) to the ranking obtained on the extensigraph. Dough extensibility data from the extensigraph and the TA, using hook speed 10.0 mm/sec, was correlated (r = 0.90) to loaf volume. Similarly, dough extensibility on the TA, using hook speed 3.3 mm/sec, was correlated to loaf volume (r = 0.96). The effect of three dough water contents (farinograph absorption, farinograph absorption + 6%, and 2‐g mixograph water absorption) on physical properties of dough were evaluated by mixing the dough in a 2‐g mixograph and testing the extensibility on the TA. Dough prepared at farinograph absorption + 6% and at mixograph absorption allowed differentiation between wheats based on the resistance to extension (Rmax).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".