Influence of particle size on flour and baking properties of yellow pea, navy bean, and red lentil flours
Bibliographic record
Abstract
Abstract Background and objectives Pulse flours are produced by dry grinding pulses. Currently, no standards exist for the particle size of pulse flours. The objective of this study was to investigate how particle size affected the flour and bread‐baking properties of commercially milled pulse flours and those produced using a Ferkar mill. Findings Finer pulse flours tended to have greater starch damage, lower water absorption capacity (WAC), and higher peak and final viscosities. Navy bean flour had a larger particle size distribution, lower starch damage, greater WAC, and lower peak and final viscosities due to presence of hull. Red lentil flour had a larger particle size distribution and higher starch damage than yellow pea flour. Bread made with finer pulse flours had better bread scores and a tighter, less open crumb structure. Bread volume was not affected by flour particle size, nor were the sensory properties of the bread in most cases. Conclusions Particle size affected flour and bread‐baking properties of pulse flours indicating that particle size should be considered when formulating pulse‐based breads. Flours milled from whole pulses will have larger particle size distributions due to the presence of hull. Seed hardness will affect the grinding properties of pulses which will affect particle size and starch damage. Significance and novelty Standardization of particle size for pulse flours would allow for consistency when sourcing flours from different suppliers. However, given that different particle size distributions may be better suited to certain applications than others, it may be more useful if suppliers specify the particle size similar to what is done with oat ingredients.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".