Influence of Sprout Damage on Oriental Noodle Appearance as Assessed by Image Analysis
Bibliographic record
Abstract
ABSTRACT Fresh alkaline (kansui) and white salted noodles prepared from sound and sprout damaged patent flours of the western Canadian wheat class Canadian Prairie Spring White (CPSW) cv. Vista were characterized by image analysis (IA). In all samples, the number of discolored spots increased with aging <24 hr (24 ± 1°C), although the number of spots per sample was significantly influenced by the degree of sprout damage. Alkaline kansui noodles made from severely sprouted wheat (Day 5) flours had the greatest number of spots per image at 1 hr (114) and increased to 256 spots per image by 7 hr. This represented an approximate fivefold greater number of spots as compared with the sound flour kansui noodle at 7hr. No further increase in spot numbers was detected in the severely sprouted sample with aging for 24 hr. Significantly fewer spots were observed in the white salted noodles (WSN) prepared from heavily sprouted wheat with 29 spots per image at 1 hr increasing to only 54.5 after 24 hr. The IA system was able to detect a significant difference in the size of the discolored spots over time due to sprout damage. The largest spot size was measured in the kansui noodles prepared from heavily sprouted wheat. All sprouted flours used to prepare both kansui and WSN had significantly larger spot sizes as compared with sound control flours. The mean darkness values for the noodle spots prepared from the heavily sprouted flours were significantly darker than the control flours for both WSN and kansui noodles. Spots of all noodles were characterized by darkness distribution profiles that highlighted key differences between noodle type and the degree of sprout damage. Addition of sodium metabisulfite to the kansui noodles at 1,000 ppm significantly decreased the number of spots formed, minimized the size, and lightened the spots over the first 7 hr, but they subsequently darkened after 24 hr.
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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 | 0.000 | 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".