Sensory Characteristics and Consumer Acceptance of Bread and Cracker Products Made from Red or White Wheat
Bibliographic record
Abstract
Whole grain consumption is being promoted due to a number of associated health benefits. However, whole grain consumption is below recommendations possibly due to the presence of characteristic flavors that consumers find unacceptable. The objective of this study was to investigate the sensory characteristics and consumer acceptance of products made from commercial whole grain flours produced from red or white wheats, and with fine or coarse bran particle sizes. Descriptive analysis and consumer acceptance panels were used to characterize both low (cracker) and intermediate (bread) moisture products made with the flours. Partial least squares (PLS) regression was used to correlate the descriptive and consumer data. Sensory differences in whole grain products made from red or white wheat with small or large bran particles sizes and product moisture contents were observed. Bran particle size had a greater effect on the sensory properties of the whole grain products, particularly within the cracker; conversely bran particle size had little influence on consumer acceptance. Red wheat products were found to be more acceptable than the white wheat products. However, a number of color × bran particle interactions were observed in both the descriptive and consumer data. PLS regression demonstrated that consumers could be divided into groupings based upon certain attributes and characteristics.
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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 | 0.001 |
| 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) | 0.002 | 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".