Development of an Orange‐Flavored Barley β‐Glucan Beverage
Bibliographic record
Abstract
ABSTRACT Barley β‐glucan concentrate shows great potential as a functional food ingredient, but few product applications exist. The objectives of this study were to formulate a functional beverage utilizing barley β‐glucan concentrate, and to make a sensory evaluation of beverage quality in comparison to pectin beverages and to assess shelf stability over 12 weeks. Three beverage treatments containing 0.3, 0.5, and 0.7% (w/w) barley β‐glucan were developed in triplicate. Trained panelists found peely‐ and fruity‐orange aroma and sweetness intensity to be similar (P > 0.05) for all beverages tested. Beverage sourness intensity differed among beverages (P ≤ 0.05). Panelists evaluated beverages containing 0.3% hydrocolloid as similar (P > 0.05), whereas beverages with 0.5 and 0.7% β‐glucan were more viscous (P ≤ 0.05) than those with pectin at these levels. Acceptability of beverages was similar according to the consumer panel. Shelf stability studies showed no microbial growth and stable pH for all beverages over 12 weeks. Colorimeter values for most beverages decreased (P ≤ 0.05) during the first week of storage, mostly stabilizing thereafter. With an increase in concentration, β‐glucan beverages became lighter in color (P ≤ 0.05) and cloudier, but these attributes for pectin beverages were not affected (P > 0.05). β‐Glucan beverages exhibited cloud loss during the first three weeks of storage. β‐Glucan can therefore be successfully utilized in the production of a functional beverage acceptable to consumers.
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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) | 0.001 | 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".