Influence of premilling thermal treatments of yellow peas, navy beans, and fava beans on the flavor and end‐product quality of tortillas and pitas
Bibliographic record
Abstract
Abstract Background and Objectives Thermally pretreating pulses prior to milling has been successful in improving sensory properties of foods formulated with them. This research investigated the effect of pretreating yellow peas, navy beans, and fava beans using roasting and micronization and examined flour quality, end‐product quality, and sensory properties when the flours were used in tortilla and pita bread. Findings Tortillas and pitas made from flours of roasted pulses were generally darker in color. Micronizing was more successful at reducing bitter flavors in tortillas and bitter and beany flavors in pitas and had a greater impact on purchase intent scores. Conclusions Minimal effects on flour and end‐product quality were observed. Beany and bitter flavors in tortillas and pitas decreased when yellow peas and navy beans were thermally pretreated prior to milling. Flours milled from micronized navy beans and yellow peas resulted in higher purchase intent scores for tortillas and pitas, respectively. Significance and Novelty Thermal pretreatments had minimal effects on pulse flour quality and improved some sensory properties of the resulting tortillas and pitas. Identifying thermal pretreatments that can improve the sensory properties provides a greater opportunity for the use of pulse flours.
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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 | 0.001 |
| 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".