Effects of roasting temperatures and storage on the quality of red lentil
Bibliographic record
Abstract
Legumes including red lentils have soluble and insoluble fibers, resistant starch, folate and proteins. The functional properties of these nutrients may reduce the risk of cardiovascular diseases and promote the well-being of pulse consumers. Roasting of red lentils can be used to produce flour, high protein and starch fractions. Dehulling of red lentils may be improved by roasting. Roasting may improve the flavor and palatability of red lentils, and may reduce anti-nutritional factors associated with legume consumption. Little is known regarding the effects of roasting temperatures and storage on the quality characteristics such as breakage susceptibility, color and hardness of roasted red lentils. ‘Robin’ red lentils at initial moisture content of 15 to 16% were roasted at temperatures of 160, 180, and 200C for 15, 30, and 45 minutes. The roasted lentils were cooled immediately. The lentils were placed in Ziploc bags and stored at temperatures of 5C and 25C. The breakage susceptibility, color and hardness of the stored lentils were measured periodically. The color of the samples was determined using Hunterlab spectrocolorimeter. The Stein breakage test was used to determine the breakage susceptibility of the lentils. The hardness of the roasted and stored lentils was measured using a texture analyzer. The results of the quality characteristics of the roasted and stored red lentils will be presented.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".