TEXTURAL PROPERTIES OF FOOD SYSTEMS HAVING DIFFERENT MOISTURE CONCENTRATIONS AS IMPACTED BY OAT BRAN WITH DIFFERENT <i>β</i>‐GLUCAN CONCENTRATIONS
Bibliographic record
Abstract
ABSTRACT Porridge and muffins were made from oat bran of two publicly available line, “Paul” and “Jim,” two experimental lines N979‐5‐2–4 (N979) and IA95111 (IA95) and a retail oat bran, to represent wet and dry food systems. N979 and IA95 had greater % β‐glucan and protein, less starch, greater water solubility index than other brans. Slurries of ground N979 bran had the greatest peak viscosity among all bran types. The adhesiveness of porridge was positively correlated and the firmness of fresh muffins was negatively correlated with % β‐glucan. No relationship was found between % β‐glucan and mouth coating of porridge during sensory evaluation. IA95 and N979 muffins were rated to have greater dome shape and coarser surface texture than Jim and Paul muffins. Although β‐glucan could increase the adhesiveness of a wet oat food and make a dry oat food less firm, the impact of β‐glucan on the sensory properties was minimal. PRACTICAL APPLICATIONS Porridge and muffins made with oat bran are popular foods among health‐conscientious people. In this study, porridge was made entirely with oat bran and water, thus delivering a high concentration of the soluble fiber. Porridges made with oat bran having different β‐glucan concentrations also differed in sensory and physical properties, perhaps providing guidance in labeling oat brans according to their functional characteristics. Muffins contained only 25.6% of oat bran; however, the impact of β‐glucan on the appearance, mouthfeel and taste of muffins was great. Thus, understanding these impacts may help producers choose appropriate food systems for delivery of high amounts of β‐glucan.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".