Impact of milling on the functional and physicochemical properties of green lentil and yellow pea flours
Bibliographic record
Abstract
Abstract Background and objectives Pulse flours with different particle sizes are available for food production. The effect of varying milling techniques on the quality characteristics of green lentil (GL) and yellow pea (YP) flours was investigated. Findings Using a Ferkar mill (one‐step vertical knife mill) caused wider distribution and bigger particle size in all flours than milling with a roller mill. The highest starch damage (3%), starch content (57%), but lowest protein content (22%) were found in YP break flours produced by roller milling. Foaming stability of pulse flours decreased to 51%–71% after 120 min. Lowest viscosities were determined in Ferkar milled flours during pasting. There was no difference in protein strength of GL flours; however, starch gelatinization was found the highest in Ferkar milled flours. In vitro starch digestibility and in vitro protein digestibility‐corrected amino acid score were not affected from milling type within each pulse type. Conclusion Different flour streams and different milling technologies caused changes in various flour characteristics. However, the particle size of roller mill flours was similar in each pulse type. Significance and novelty The obtained results are a crucial step in better understanding the functional properties and possible food applications of pulse flours according to different milling streams and particle sizes.
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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".