Gluten protein network formation induced by a novel laminated sheeting process: Effect on the textural stability of cooked noodles
Bibliographic record
Abstract
To address the progressive textural deterioration of takeaway noodles during immersion, this study employed a novel laminated sheeting process for noodle preparation by integrating dough sheet with dough crumbs. The effects of the ratio of dough sheet to crumbs (R-DSTC) (1:0, 1:0.25, 1:0.75, and 1:1.5) and sheeting ratio (R-S) (20 %, 30 %, and 40 %) on the textural stability of cooked noodles were investigated. Both cooking quality and textural characteristics of noodles improved with increasing R-DSTC and R-S values. The highest hardness (4894.62 ± 92.39 g) and tensile distance (72.91 ± 3.24 mm) were observed under the conditions of R-DSTC-1:0.75 and R-S-30 %. Afterwards, noodles exhibited relatively less textural deterioration during immersion. Confocal laser scanning microscopy (CLSM) confirmed that the R-DSTC-1:0.75 and R-S-30 % combination produced a denser protein network, with higher gluten junctions (921.25) and lower lacunarity (7.48). Magnetic resonance imaging (MRI) findings indicated that a compact gluten network effectively inhibited water penetration.
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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 | 0.000 | 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) | 0.000 | 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 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".