Assessment of Oriental Noodle Appearance as a Function of Flour Refinement and Noodle Type by Image Analysis
Bibliographic record
Abstract
ABSTRACT Fresh alkaline and white salted noodle sheets prepared from patent and straight‐grade flours of the western Canadian wheat class Canadian Prairie Spring White (CPSW), Karma and Vista, were visually characterized by image analysis over a 24‐hr period. In both cultivars, the number of specks increased with time although the actual numbers were significantly influenced by both detection size and sensitivity. Maximum speck generation was observed in Karma's straight‐grade kansui noodle sheets, increasing from 12.9 specks/cm2 at 1 hr to 58.0 after 24 hr. Lowest speck numbers were observed in Vista's patent white salted noodle sheets with 4.5 specks/cm2 at 1 hr increasing to 5.6 after 24 hr. The image analysis system was able to show that in combination with a significant cultivar effect, both flour refinement and noodle type significantly influenced the number of discolored specks detected over time. Straight‐grade flours yielded more specks than the patent flours, while salted noodle sheets consistently had fewer specks compared with their kansui noodle sheets at all time intervals. No differences were detected in the average size of the specks due to cultivar or noodle type in the patent flour noodle sheets. Noodle sheets made from Karma straight‐grade flour had significantly larger specks than noodle sheets made from Vista's straight‐grade flour for both noodle types. Patent flour kansui specks were lighter than their salted counterparts. Straight‐grade noodle specks were darker than their corresponding patent flours, but this difference was significant only in the kansui noodle sheets. Specks of all noodle sheets were characterized by darkness distribution profiles that highlighted key differences between the wheat cultivar samples due to noodle type and flour refinement.
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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 | 0.001 | 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".