[Progress in the research and application of wheat grain protein].
Bibliographic record
Abstract
L.) is one of the main staple food crops in China. The protein quality of wheat grains directly determines the processing quality and nutritional quality of flour foods. With the growth of the population, the upgrading of the dietary structure, and the transformation of the demand in the food industry, the research on wheat in China has shifted from simply pursuing high yields to a new stage of coordinated improvement of yield, quality, and nutrition. This article systematically reviews the main progress in the research on the quality of wheat grain protein, including the identification of gluten proteins and high-quality subunits, the analysis of the expression regulation of gluten protein genes, the mining of quality-related genes based on multi-omics, the impact of the interaction between proteins and other components on processing characteristics, and the application of biotechnology in the breeding of high-quality wheat. In view of the complex evaluation process and environmental susceptibility of wheat quality traits, as well as the goal of achieving synergistic optimization of nutrition and functionality in protein quality research under the National Whole Grain Action Plan, we examine the challenges and future development prospects of cutting-edge technologies such as marker-assisted selection and gene editing. This review aims to provide theoretical support for the upgrading of the high-quality wheat industry.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".