Computer simulation analysis to improve radio frequency heating uniformity of multi-component rice samples by screening of sample structure
Bibliographic record
Abstract
Ready to eat multi-component foods are getting increasingly popular in diet modern formulations, but requires an efficient reheating method that results in rapid and uniform heating of the designed food sample. In this study, a computer simulation model based on commercial COMSOL software was developed for the efficient operation of a custom-built small-scale 50 Ω radio frequency (RF) heating system. The reliability of the simulation model was first verified using RF heating of two types of single-component samples (cooked rice and sausage) at different electrode gaps. Subsequently, the pre-validated model was used to explore the heating effects of multi-component rice schemes with the same volume and different shapes. A relatively synchronized heating procedure was developed for the formulated sample with the transformation from cube to cylinder which was characterized by the migration of the relatively high and low temperature regions. The RF heating of a comprehensive sample structure design was simulated. The experimental results showed that a better heating uniformity (lower λ = 0.044) and relatively high energy efficiency ( η = 70.5%) were obtained in the verified IR30-ER40 scheme as compared to the two types of single-component cuboid samples. This study of controlling the absorption and utilization of RF energy in various parts (component and location) for the multi-component sample scheme with a systematic structure design can provide some useful guidance in improving heating uniformity of multi-component food products. • A computer model for an RF heating system was established for multi-component rice schemes. • More uniform heating was expected in sample shape transformation from cuboid to cylinder. • IR30-ER40 scheme was expected to have better RF heating uniformity, shorter process time, and higher energy efficiency. • Effective and feasible RF heating configuration for multi-component food could be obtained through a systematic study.
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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) | 0.002 | 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".