Textural and Other Quality Properties of Instant Fried Noodles as Affected by Some Ingredients
Bibliographic record
Abstract
ABSTRACT The effects of varying the proportion of three noodle dough components (water, gum, and starch) on the texture (maximum load and strain at break), amount of fat absorbed, and percent rehydration of instant fried noodles were studied. The Instron Universal testing machine was used to measure noodle texture, whereas quality attributes were determined using fat absorption and rehydration parameters. The results showed that changes in maximum load, strain at break point, fat absorption, and rehydration% of instant noodles depended on interactions between the ingredients. Increasing the gum content, starch content (for amounts >4% kg/kg of flour) and moisture content (35–40% kg/kg of flour) enhanced the elasticity and extensibility of cooked instant fried noodles. Addition of starch decreased fat absorption but showed mixed effect on rehydration%. The effect of gum addition at 0.1, 0.2, and 0.3% on fat absorption was significant but reduced considerably or showed a reverse effect at higher starch addition levels. Increasing moisture, and gum contents increased rehydration% of cooked instant noodles. Appropriate combinations of gum, starch and moisture contents could be used to optimize textural and quality characteristics of fried instant 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".