Frozen Convenience Noodles: Use of Ultrasound to Study the Influence of Preparation Methods on Their Rheological Parameters
Bibliographic record
Abstract
Longitudinal ultrasonic waves were used to investigate effects of preparation and frozen storage time on the rheological properties of noodles. Noodles prepared with and without glucose oxidase (GOx) were flash frozen either immediately after production (raw), after blanching, or after being optimally cooked. From measurements of attenuation, phase velocity, loss and storage moduli (M″ and M′), and tanδL (M″/M′), it was found that raw noodles, prepared with or without GOx, were most similar overall to fresh noodles when stored frozen for one week. However, blanched noodles were closest to fresh noodles in terms of firmness, as measured by storage modulus, after one week of storage. Frozen storage for four weeks resulted in a significant loss in noodle quality. Stress relaxation measurements of Peleg's K1 and K2 parameters showed a significant effect of GOx addition on raw noodles after one week of frozen storage. As shown by K1 and K2 parameters, GOx addition delayed noodle texture deterioration associated with frozen aging for blanched and cooked treatments. The combined texture assessment of stress relaxation coupled with simultaneous ultrasonic measurements exhibits good potential for examining how formulation and pretreatment can mitigate the textural quality impairments associated with freezing of convenience noodles.
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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".