FLAVOR PROPERTIES OF PAN AND PITA BREADS MADE FROM RED AND WHITE HARD SPRING WHEATS
Bibliographic record
Abstract
ABSTRACT Red and white wheat comparisons have not always shown consistent flavor differences. The objective was to compare flavor properties of whole wheat pan and pita breads made from white and red Canadian hard spring wheats. Flavor attributes were defined by trained panelists who marked intensities using 15‐cm line scales. Pan bread made from BW275 and Kanata had significantly lower wheat and wheaty aftertaste intensity than AC Domain (red) and RL4863. Pita bread from Kanata had significantly higher sweet intensity than RL4858, Snowbird and AC Domain (red). Principal component analysis accounted for 74–75% of the variance in both bread types. Biplots showed that some of the white wheat breads tended to be sweeter with less wheaty and bitter with wheaty, bitter and sour aftertastes. Red wheat breads tended to be less sweet, more wheaty and bitter with wheaty, bitter and sour aftertastes. Higher wheaty flavor intensity was associated with visually darker breads. PRACTICAL APPLICATIONS The study revealed that there were distinct flavor differences in pan bread and pita bread made from red and white wheats. The sweeter and milder flavor observed for some white wheats could be a marketing advantage for these newly developed wheats. In pan and pita bread products targeted to consumers that dislike the often wheaty and bitter taste of whole wheat products made from red wheat, white wheat derived whole wheat products with their milder taste may be more acceptable.
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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 | 0.001 | 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) | 0.001 | 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".