Effects of Storage Temperature, Time, and Pre-Treatments on Dehulling Quality of Red Lentils
Bibliographic record
Abstract
Abstract: Red lentils produced in Canada undergo biochemical changes when exported to countries with different climates. This may adversely affect their end-use as chemical composition affects dehulling quality. Therefore, there is a need to develop a knowledge base on the effects of storage conditions and storage pre-treatments on dehulling quality of Canadian red lentils. In this study, effects of storage time (up to 12 months), storage temperature (5 and 25oC), and storage pre-treatments (moisture tempering, drying, cycles of freezing-thawing, and cycles of rewetting) on dehulling quality of red lentils (Redberry cultivar) were investigated. The rewetting cycles had the most deleterious effect on dehulling efficiency of lentils, evident especially after 12 months of storage. The samples subjected to freezing and thawing cycles possessed dehulling efficiency values that were most similar to those of the un-treated stored lentils at the 12 months storage period. Storage time had the most significant effect on the dehulling parameters of the pre-treated samples stored for 12 months. In most cases, storage moisture content was the second most important contributor affecting the dehulling parameters of lentils. Storage temperature had a marginal effect on dehulling efficiency of stored red lentils subjected to the moisture tempering and drying pre-treatments.
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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".