The enrichment of Asian noodles with fiber‐rich fractions derived from roller milling of hull‐less barley
Bibliographic record
Abstract
Abstract Fiber‐rich fractions (FRF) derived from roller milling of waxy (W) and high amylose (HA) starch hull‐less barley genotypes were evaluated for suitability as functional ingredients in fresh and dried white salted (WSN) and fresh yellow alkaline (YAN) noodles. FRF‐W and FRF‐HA both contained over 300 g kg−1 dietary fiber, and over 200 g kg−1 of β‐glucans. Replacement of 250 g kg−1 Canada Prairie Spring White (cv AC Vista) wheat patent flour with the FRF posed no problems in noodle processing, although water absorption had to be substantially increased. All three noodle types enriched with the FRF were significantly darker and contained more brown specks than the wheat flour control noodles. The presence of the FRF reduced cooking time of fresh YAN and WSN by ∼50%. The addition of FRF improved cooked YAN texture, as evidenced by increased firmness and resistance to compression. FRF‐enriched fresh WSN were comparable to the wheat flour control noodles for those parameters, whereas enrichment of dry WSN by FRF imparted less firmness and less chewiness. FRF‐enriched fresh YAN and WSN offer consumer convenience due to shorter cooking time, improved nutritional quality and acceptable cooking quality. These features might make FRF‐enriched noodles sufficiently attractive to health‐conscious consumers to overcome the negative effects of color and appearance Copyright © 2005 Society of Chemical Industry
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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".