Relationship between flour physico-chemical properties and dumpling sheet quality.
Bibliographic record
Abstract
【Objective】 The objective of this study is to investigate the effects of flour properties on dumpling sheet quality. 【Method】 Fifteen samples including wheat cultivars from China and Canada,commercial wheat samples from Australia and USA and commercial flours from China were used to measure flour physico-chemical properties,amylose content,Rapid Visco Analyser(RVA) parameters and dumpling sheet quality. 【Result】 Farinograph development time and stability,and extensograph extension area were significantly correlated with raw dumpling sheet color (r=0.60,0.55,0.47,respectively,P0.05),and negatively correlated with cooked dumpling sheet elasticity (r=-0.55,-0.59,-0.48,respectively,P0.05). Extensograph maximum resistance was highly and significantly correlated with raw dumpling sheet color,appearance and stickiness of cooked dumpling sheet (r=0.64,0.72,0.67,respectively,P0.01),and negatively correlated with elasticity and hardness score of cooked dumpling sheet (r=-0.81 and -0.72,respectively,P0.01). Starch content was highly and negatively correlated with stickiness of cooked dumpling sheet (r=-0.68,P0.01). Amylose content was negatively correlated with elasticity of cooked dumpling sheet (r=-0.60,P0.05). RVA analysis indicated that hot pasta viscosity,final viscosity and pasting time had the greatest influence on dumpling sheet stickiness (r=0.61,0.54 and 0.53,respectively,P0.05). Breakdown was positively correlated with dumpling sheet firmness (r=0.58,P0.05). 【Conclusion】 Gluten strength parameters contributed negatively to elasticity and firmness and positively to appearance of dumpling sheet. The firmness and stickiness of dumpling sheet was negatively associated with starch content,and amylose content was negatively associated with dumpling sheet elasticity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".