The impact of mechanical scouring and moisture conditioning on the in vitro protein digestibility and quality of roller‑milled green lentil (<i>Lens culinaris</i>) and yellow pea (<i>Pisum sativum</i>)
Bibliographic record
Abstract
Abstract Background and Objectives Milling practices, otherwise refined for specific uses in cereal‐based foods, have not been thoroughly developed for pulses. This study investigates whether scouring and moisture conditioning pretreatments on yellow peas and green lentils can enhance hull removal, and in turn, whether changes in hull removal alter in vitro protein digestibility and quality. Findings Total by‐product losses were significant in green lentils when subject to scouring, which was altered by high moisture addition in yellow peas. The scouring pretreatment altered both the protein digestibility and amino acid scores of green lentils, which translated to improved protein quality in all streams, but significantly in the break flour stream. Yellow peas similarly demonstrated significant improvements in protein quality from scouring, as a result of altered amino acid scores. Conclusion The addition of a scouring procedure can improve the protein quality of yellow peas and green lentils. Significance and Novelty Pulse milling procedures are rarely evaluated for optimization of protein quality. This research establishes milling protocols that may be used to enhance the protein quality of yellow peas and green lentils.
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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 | 0.000 | 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) | 0.001 | 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".