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
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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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 | 0.000 | 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 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".