Molecular and biochemical screening of Turkish durum wheat landraces for γ-gliadin and lmw-glutenin proteins associated with pasta-cooking quality
Bibliographic record
Abstract
In recent years, pasta-cooking quality has become an important issue in durum wheat breeding. Pasta-cooking quality of durum wheat has been shown to depend mainly on protein content and gluten properties. Gluten is a complex mixture of proteins composed of gliadins and glutenins. A strong correlation exists between certain ?-gliadin and/or LMW-glutenin proteins and the viscoelastic properties of gluten affecting al dente cooking quality of pasta goods. Of those proteins, ?-gliadin 45 and LMW-2 glutenin alleles are correlated with proper gluten strength and superior pasta-cooking quality, whereas ?-gliadin 42 and LMW-1 glutenin tend to provide weak gluten with reduced cooking quality. In this study, DNA and protein markers have been jointly used for the analysis of ?-gliadin and LMW-glutenin QTLs of Turkish local durum wheat cultivars (landraces) affecting pasta-cooking quality. For that purposes, 13 SSR, one STS and two GAG primers linked to Gli-B1 loci were used. Polymorphic relations of 28 Turkish durum wheat landraces with Canadian durum wheat cultivars of Kyle and Avonlea were determined through PCR reactions. Additionally, gliadin and LMW-glutenin proteins of the landraces were separated using A-PAGE and SDS-PAGE techniques, respectively. Of the 28 durum landraces, 17 were determined carrying ?-gliadin 45 and LMW-2 glutenin proteins associated with proper gluten strength and superior pasta-cooking quality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".