Environmental conditions affect semolina quality in durum wheat (<i>Triticum turgidum</i> ssp. <i>durum</i> L.) cultivars with different gluten strength and gluten protein composition
Bibliographic record
Abstract
BACKGROUND: Sowing time may impact semolina and pasta cooking quality by changing the environmental conditions during grain filling. The effect of an optimum and a delayed sowing time on semolina quality was studied by comparing six cultivars under irrigation, in order to isolate temperature from drought effects. RESULTS: Protein content was higher in the old cultivars and in the late sowings, according to the number of days with temperature between 30 and 40 °C during ripening. Gluten index increased as temperature rose to a threshold of about 30 °C, then decreased under higher temperatures. Mixograph parameters were less sensitive to high temperatures. Gliadin:glutenin correlated with gluten strength. Spaghetti firmness and protein content were positively correlated independently of sowing date. Cultivars Trinakria and Cappelli had the highest spaghetti firmness (900 and 828 g). CONCLUSIONS: Late sowings may represent a way of increasing pasta cooking quality whenever they place grain filling under thermal conditions able to increase protein percentage, although the accompanying decrease in yield may represent a drawback in environments prone to drought stress during ripening. The lower protein percentages of modern durum wheat cultivars under conventional sowing times results in a lower pasta cooking quality despite higher gluten strength.
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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 | 0.000 |
| 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".