Effect of variety and crude protein content on dehulling quality and on the resulting chemical composition of red lentil (<i>Lens culinaris</i>)
Why this work is in the frame
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Bibliographic record
Abstract
Abstract BACKGROUND: Dehulling is one of the most important operations in post‐harvest handling of red lentils ( Lens culinaris ). However, little information is available on how variety and crude protein content affect the dehulling quality characteristics and on how dehulling affects chemical composition of red lentils. Therefore, the main objective of this work was to investigate the effect of variety and crude protein content on dehulling quality and on the resulting chemical composition of red lentils. RESULTS: Four varieties of red lentil, each with two levels of protein content, were selected for this study. Crude protein content overall ranged from 225.7 to 311.7 g kg −1 dry matter. Results indicated that variety and crude protein content had a significant effect on dehulling efficiency (DE), powder produced, broken seeds (BRK) and hull removed. Dehulled seeds exhibited higher protein, starch, phytic acid, stachyose and verbascose content, but lower TIA, tannin, sucrose and raffinose content than raw seeds. CONCLUSION: Variety and protein content had a significant effect on DE. Dehulling affected chemical composition of lentils. DE was positively correlated with starch content but negatively correlated with protein and crude fiber content of raw seeds. Information gathered from the study will be useful for lentil breeders, processors and marketers. Copyright © 2008 Society of Chemical Industry
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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 | 0.001 | 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 | 0.001 |
| 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