Processing and Quality Evaluation of Dumplings
Bibliographic record
Abstract
In order to establish a protocol for dumpling processing and quality evaluation method in laboratory,fifteen flour samples from wheat areas in China,Canada,Australia and USA were selected as the subjects in this study.The effects of mixing speed and time,water-absorbing capacity of dough,resting time and sheeting gaps on quality of dumpling were examined.Results indicated that mixing at the speed of 68 r/min in a GRL-1000 mixer for 12 min,40%-47% water-absorbing amount for flour,two-step dough mixing method including first step mixing for 10 min followed by 5 min of resting and second step mixing for 2 min followed by 5 min of resting,seven-step dough sheeting including the gap settings of 5.0,4.0,3.0,2.0,1.5,1.2mm and 1.0 mm generated the dough with optimal quality for dumpling processing.Raw and cooked dumpling wraps were evaluated from appearance and texture through a sensory panel and instruments.A positive correlation between the color of raw dumpling skin L*1h and the color evaluated by sensory panel was observed(r = 0.79,P 0.01);Similarly,a positive correlation between the elasticity index of cooked dumpling determined by instruments and the elasticity index evaluated by sensory panel was also achieved(r = 0.88,P 0.01).However,a difference in appearance and texture of dumplings was exhibited due to different resources of flour.Although Chinese wheat flour provided good elasticity during dumpling preparation,acceptable firmness of dumplings during cooking was not exhibited.Australian and American flour offered good color and elasticity for dumplings,but processed dumplings exhibited soft texture.Canadian wheat flour was characterized by good appearance,acceptable elasticity,and better firmness and texture during cooking.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".