Quality Characteristics of Yellow Alkaline Noodles Enriched with Hull‐less Barley Flour
Bibliographic record
Abstract
ABSTRACT Roller milled flours from eight genotypes of hull‐less barley (HB) with normal, waxy, zero amylose waxy (ZAW), and high amylose (HA) starch were incorporated at 20 and 40% (w/w) with a 60% extraction Canada Prairie Spring White (CPSW, cv. AC Vista) wheat flour to evaluate their suitability as a blend for yellow alkaline noodles (YAN). The barley flour supplemented noodles were prepared using conventional equipment. Noodles containing 40% HB flour required less work input than the corresponding 20% blend noodles due to a higher water absorption at the elevated level of HB flour addition, which probably caused them to soften. The addition of any HB flour at either level to the CPSW flour resulted in significantly decreased brightness (L*) and yellowness (b*), elevated redness (a*), concomitant with a significantly greater number of specks per unit area of noodle sheet compared with the control flour. The addition of 40% HB flour to YAN decreased cook time and cooking losses. Noodle firmness, as determined by maximum cutting stress (MCS), was significantly increased by the addition of 40% HB flour. Noodle chewiness, as determined by the texture profile analysis (TPA), was affected by the type of starch in the barley samples; the addition of waxy and ZAW HB flour decreased chewiness, whereas normal and HA HB flour increased chewiness of composite 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 | 0.000 |
| 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".