Effect of variety and crude protein content on dehulling quality and on the resulting chemical composition of red lentil (<i>Lens culinaris</i>)
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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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 | 0.001 | 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".