Kinetics of a thin‐layer microwave‐assisted infrared drying of lentil seeds
Bibliographic record
Abstract
Abstract Herein, we report the drying kinetics behavior of tempered lentil seeds (CDC Maxim variety) by utilizing a microwave‐assisted infrared thermal method and thereby presenting a successful mathematical model for it. The drying characteristics of lentils using thin‐layer microwave drying with and without hot air predrying were evaluated in a laboratory scale microwave dryer. The drying experiments were carried out at 300 and 750 W, and the predrying experiment was performed at room temperature (23°C). Out of several thin‐layer mathematical models evaluated with the experimental data, Page model has been found the most appropriate model to predict drying process of lentils with high value of coefficient of determination (0.995), low values of chi‐square (0.0012), root mean square error (0.0343), and mean relative percentage error (4.9997). Further, the influence of bioyield force and changes in the particle density of processed seeds have also been evaluated in the present study. The results showed that combination of low infrared power (0.375 kW) to the different microwave power levels led to a significant reduction of drying time. The results also showed that processing of lentil seeds significantly reduces the bioyield force of raw seeds, providing less firmness to the product and thereby shortening the cooking time. The above findings can facilitate the design and operation of infrared‐assisted microwave drying of other legumes.
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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".