Flour Particle Size, Starch Damage, and Alkali Reagent: Impact on Uniaxial Stress Relaxation Parameters of Yellow Alkaline Noodles
Bibliographic record
Abstract
ABSTRACT Three patent flours, each possessing three different levels of starch damage were prepared from a single hard white spring wheat. Each flour was sieved to yield three flours with different particle size distributions (85–110, 110–132, 132–183 μm). Raw alkaline noodles were prepared from the nine flours using either 1% w/w kansui (sodium and potassium carbonates in 9:1 ratio) or 1% w/w sodium hydroxide. Uniaxial stress relaxation parameters percent stress relaxation (SR%), initial rate of relaxation ( k 1 ) and the extent of relaxation ( k 2 ) were measured on the raw noodles immediately after production ( t = 0 min) and at 60 min. Raw noodles after resting for 60 min were optimally cooked and stress relaxation parameters were measured. Raw noodles at t = 0 min exhibited SR%, k 1 , and k 2 that were significantly ( P < 0.0001) influenced by both the degree of starch damage and the type of alkaline reagent used. Flour particle size only influenced SR% and k 1 ( P < 0.025) but had no impact on k 2 . In raw noodles aged for 60 min, both SR% and k 2 were significantly influenced by alkaline reagent, particle size, and starch damage ( P < 0.01) while k 1 was only affected by the degree of starch damage ( P < 0.0001). Cooked noodle SR parameters were all significantly ( P < 0.0001) influenced by alkaline reagent, particle size, and the degree of starch damage. Cooked noodles prepared from starch with low damaged flours within any given particle size range, regardless of the type of alkali employed, yielded the most rheologically elastic‐like (firmer) noodles. Two potential mechanisms by which the degree of starch damage influences noodle elastic like texture are discussed.
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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 | 0.001 |
| 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".