Developing a high‐throughput micromilling protocol for evaluating durum wheat milling performance and semolina quality
Bibliographic record
Abstract
Abstract Background and objectives Effective and efficient selection of key quality traits is crucial to develop new durum varieties with improved end‐use quality. This study was undertaken to develop a rapid micromilling protocol using a single Brabender Quadrumat Jr. (QJ) semolina mill without purification to predict milling performance and generate semolina for quality analysis. Findings After grinding 200 g of durum wheat with a QJ mill with the reel sifter removed, the resulting wholemeal was sifted through a laboratory sifter equipped with a bottom screen of 180 µm to remove flour and a top screen of 630 µm to retain bran‐rich fraction. Semolina materials between the two screens were collected. A model for predicting semolina yield (R2 = 0.81) was developed based on the amounts of semolina and bran‐rich fraction, thus eliminating the need for additional milling to recover semolina in the bran‐rich fraction. There were highly significant correlations (r > 0.86) for semolina ash, yellowness, yellow pigment content, protein content, wet gluten, and gluten index between semolina prepared with this proposed protocol and those generated with a Allis‐Chalmers mill. Conclusions The micromilling protocol developed in this study is rapid and reliable for assessing durum milling performance and for preparing semolina for quality characterization. Significance and novelty The proposed protocol modified QJ mill and optimized milling conditions. A robust and simplified model was developed for predicting semolina yield with one‐step milling. It is a useful tool for breeding programs or genetic mapping studies that are usually large in sample number but very limited in sample size.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".