Incorporation of pulse flours of different particle size in relation to pita bread quality
Bibliographic record
Abstract
BACKGROUND: To increase pulse consumption, pita bread was fortified with pulse flours milled from green lentils, navy beans and pinto beans, which were ground to produce fine and coarse flours. Pita breads were prepared using composite flours containing pulse flours (25, 50, 75%) and wheat flour or 100% pulse flours and adjusting the amount of water required for mixing based on farinograph water absorption. Pita bread quality was evaluated according to diameter, pocket height, specific loaf volume, texture and crust colour. RESULTS: Blends made from pulse flours with coarse particle size showed higher rates of water absorption. All composite flours and 100% pulse flours produced pitas with pockets, confirming their suitability for this product. Crust colour of pitas was affected less by navy bean flour than by lentil flour. Pita breads made with pinto bean flour were superior in texture. Overall, navy and pinto bean flours appeared more suitable for pita bread. Flours with coarse particle sizes produced pitas with better colour and texture. Sensory parameters of pitas containing 25% coarse pinto or navy bean flour were as good as or better than those from the wheat control. CONCLUSION: Acceptable pita breads can be made using pulse flours, although the substitution level is limited to 25%.
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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".