Influence of particle size on flour and baking properties of yellow pea, navy bean, and red lentil flours
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 it