Effects of Salt and Alkaline Reagents on Dynamic Rheological Properties of Raw Oriental Wheat Noodles
Bibliographic record
Abstract
ABSTRACT To gain further understanding of the functionality of ingredients in oriental wheat noodles, the rheological properties of raw noodles made using high protein (Red Bicycle) or low protein (Sandow) wheat flours and various additives (salt or alkaline reagents at concentrations of 0, 0.1, 0.5, 1.0, 2.0, 3.0, and 4.0%) were investigated using frequency sweep and temperature sweep oscillatory tests. Generally, both the elastic modulus( G ′) and viscous modulus ( G ″) of raw noodles increased when various levels of salt or alkaline (kansui and NaOH) reagents were included in the formulation, with the exception of Red Bicycle noodles where the G ″ was not significantly affected by the salt. The G ′was significantly decreased in the presence of sodium chloride at concentrations ≤4.0% and kansui at <0.5%. The change in rheological properties of raw noodles was related to the wheat flour quality, type, level of additive, and frequency. The G ′, G ″, phase angle, and complex viscosity changed in a similar pattern when raw noodles were heated from 25 to 100°C. These parameters decreased initially with increasing temperature until they reached a valley and then increased either to a plateau or continuously in noodles containing kansui. The appearance of valley points at 75.5 and 77.2°C during heating of Sandow and Red Bicycle noodles containing salt, and 89.4, and 83.2°C during heating of Sandow and Red Bicycle noodles containing kansui, respectively, was not associated with starch gelatinization as determined using differential scanning calorimetry. The continuous increase in G ′, G ″, and complex viscosity observed with noodles containing kansui during the hold period at 100°C was attributed to the high pH environment and not to the inactivation of α‐amylase.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".