Influence of nitrogen fertilizer application rate on winter wheat (<i>Triticum aestivum</i> L.) flour quality and Chinese noodle quality
Bibliographic record
Abstract
Abstract BACKGROUND: Understanding wheat flour noodle quality responses to N management will improve the quality of recommendations made to growers for specific end‐uses. Two winter wheat cultivars, with six N rates, planted during the course of a 2‐year experiment were used to determine the effect of N application rate on Chinese white noodle quality. RESULTS: Wheat flour protein content, development time (DT) and stability time (ST) increased with N application rate, maximal at 360 kg N ha−1 and decreasing thereafter. When the N fertilizer application rate changed from 0 to 270 or 360 kg N ha−1, redness (a*) and yellowness (b*) of both flour and noodle increased, while brightness (L*) decreased. The hardness and chewiness of cooked noodles improved at lower N application rate and degenerated at higher N rate. CONCLUSION: A low rate of N fertilizer application (from 0 to 270 kg N ha−1) improved flour quality. Flour protein content and protein quality parameters displayed a significant negative correlation with brightness (L*) values. Generally, high flour protein content and protein quality, as well as good noodle texture, could be achieved by topdressing N fertilizer (in the range 270–360 kg N ha−1). Copyright © 2009 Society of Chemical Industry
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".