Pea and lentil flour quality as affected by roller milling configuration
Bibliographic record
Abstract
Abstract This study examined the effects of roller mill configuration on pulse flour quality. Dehulled yellow pea and green lentil were ground to flour using a laboratory roller mill characterized by its flexibility to control particle size reduction while maintaining a constant feed rate. The milling diagram length (long, six passes vs. short, four passes) and sieve sizes (large, 300 μm vs. tight, 180/150 μm) were adjusted for a total of four milling configurations. Each flour stream was characterized with respect to its physical properties and chemical composition. No notable differences were identified between pea and lentil based on how the milling configuration influenced flour characteristics. Overall, combining streams to produce a whole flour did not affect the chemical composition but resulted in variability for physical characteristics as indicated by a tendency toward increased levels of damaged starch with the shorter milling diagram. Damaged starch content was found to be indirectly associated (p < 0.05) with the particle size distribution, where the highest concentrations were noted in flours with median distributions below 30 μm. When individual streams were compared across milling configurations, the stream itself was rarely found to significantly influence flour physicochemical properties. However, the variation exhibited in particle‐size distribution, protein, starch, ash, and damaged starch content could have practical relevance given the many significant (p < 0.05) correlations with functional properties that could subsequently affect the end‐use applicability of flours. This would imply that specialized flours could be made with the intention of being used for defined food applications.
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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 | 0.001 |
| 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".