Effect of premilling treatments on the functional and bread‐baking properties of whole yellow pea flour using micronization and pregermination
Bibliographic record
Abstract
Abstract Background and objectives There is interest in partially replacing wheat flour with pulse flours in bread. However, flavor of pulse flours may be detrimental to the final product. Processing pulses prior to milling, using micronization and pregermination (early seed germination without radicle emergence), was investigated as a way to improve the flavor of yellow pea flour while maintaining or improving flour functionality. Findings Micronization and pregermination of peas prior to milling resulted in changes to flour particle size, color, and compositional and functional properties of the flours. Peas tempered to 18%–20% and micronized to 105–110°C produced a flour that was similar in baking properties to the flour milled from untreated peas with the exception of crumb firmness and aroma and flavor of the bread. All bread made from micronized peas tended to have reduced crumb firmness and improved aroma and flavor properties compared to bread made with untreated peas. Results for the pregerminated peas showed that the flour had higher starch damage and WAC and lower peak and final viscosities compared to the flour milled from untreated peas. Bread baked from pregerminated peas had lower bread quality in terms of bread scores, volume, crumb color, and C‐cell properties, but the bread had reduced crumb firmness and improved aroma and flavor properties, compared to bread made with untreated peas. Conclusions Both micronization and pregermination were suitable premilling treatments for yellow peas. Pregermination, however, warrants additional research to determine whether flour and baking properties can be improved. Significance and novelty Pretreating yellow peas using either micronization or pergermination prior to milling were successful in reducing undesirable flavors associated with pea flour. Depending on the treatment and conditions used, flour functionality and bread‐baking properties were maintained.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".