Effect of roasting as a premilling treatment on the functional and bread baking properties of whole yellow pea flour
Bibliographic record
Abstract
Abstract Background and objectives There is a growing global interest in partially replacing wheat flour with pulse flours in foods, including bread. However, undesirable flavors associated with pulse flours, especially yellow pea flour, have limited their use in foods. Pretreating pulses prior to milling offers a possible solution for improving the flavor of pulse flours. The objective of this research was to examine the effect of oven roasting and Revtech roasting (with and without steam) on the compositional, functional, and bread baking properties of whole yellow peas. Findings Regardless of the roasting method used, a roasting temperature of 120°C resulted in flours that retained good functionality and bread baking properties with less detrimental changes in flour color. Bread made with peas roasted at 120°C also had reduced aroma and flavor properties compared to bread made with untreated peas. Conclusions The strong aroma and flavor properties of yellow peas can be reduced by pretreating the peas prior to milling using conventional oven roasting and Revtech roasting. By selecting the appropriate roasting temperature, flour functionality for bread baking can be maintained. Significance and novelty Roasting is a useful premilling treatment for yellow peas. Reducing the off‐flavors associated with pulses while maintaining flour functionality will allow for greater use of pulse flours in formulating foods.
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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) | 0.002 | 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".