Thermal properties of lentil and chickpea in relation to radio frequency heat treatment
Bibliographic record
Abstract
Thermal properties of lentil (Lens culinaris) and chickpea (Cicer arietinum L.) were determined experimentally and with predictive mechanistic models as functions of temperature and moisture content (four levels). Thermal conductivity (k), specific heat (Cp), and density (ρ) of the samples were evaluated using a line heat source probe, differential scanning calorimeter (DSC), and pycnometer, respectively. Except for Cp which was measured at a temperature range of 30 to 90°C, other properties were measured at room temperature. Specific heat of the samples increased linearly with moisture content (MC) and temperature, ranging from 0.824 to 2.433 kJ/kg K and 0.444 to 2.067 kJ/kg K for lentil and chickpea, respectively. Thermal conductivity increased with MC in all samples with its values ranging from 0.161 to 0.191 W/m K for lentil and 0.160 to 0.227 W/m K for chickpea. However, thermal conductivity values of flours were higher at lower MC levels when compared to seeds at higher MC levels. Thermal diffusivity, (0.159 × 10−6 to 0.221 × 10−6 m2/s and 0.163 × 10−6 to 1.175 × 10−6 m2/s for lentil and chickpea flours, respectively) was calculated from known values of k, Cp, and density (ρ), with its values decreasing as the MC levels increased. Thermal properties data from our experiments did not fit into the components-based mechanistic models. Models generated in this study have good significance (p< .05 and R2 ≈ 1), meaning they can be used for prediction of these properties, as well as modeling and simulation of thermal behavior of pulses during conventional or radio frequency (RF) heating.
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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) | 0.001 | 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".