Combination of germination and innovative microwave-assisted infrared drying of lentils: effect of physicochemical properties of different varieties on water uptake, germination, and drying kinetics
Why this work is in the frame
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Bibliographic record
Abstract
Soaking, germination, and thermal processing of lentils are common treatments to improve the functional and nutritional properties and expand its usage as an affordable plant-based protein in other food applications. In this work, combinations of these procedures have been studied by investigating the hydration, germination, and dehydration behavior of three commercial varieties of lentils; Maxim, Imvincible, and Greenland, with respect to the effect of their physicochemical and mechanical properties on these behaviors. The novel and efficient microwave-assisted infrared heating has been employed for dehydration, and drying kinetics were evaluated by fitting various thin-layer models. For this reason, lentil seeds were soaked for 16 hours, germinated for the duration of 1, 2, and 3 days, and thermally processed at 0.14, 0.42, and 0.7 kW microwave power, and 0, 0.375, and 0.75 kW infrared power. The results revealed that Greenland had the highest water uptake (110.89 g of water/100 g of seeds) due to its larger size, higher bulk porosity, and less fat content. This higher water uptake by Greenland leads to a more significant drop in its bio yield force after soaking. Furthermore, the diffusion approach model was the best equation to describe the microwave-infrared drying process of all the varieties. Microwave power had the most impact on drying rate, followed by infrared power, while the effect of germination time was not significant. The value of specific energy consumption varies by microwave and infrared powers and has the least amount for microwave power of 0.42 kW and infrared power of 0.375 kW.
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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 | 0.000 |
| 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 it