Sensitivity analysis of system condition, sample structure, and material property to radio frequency heating characteristics of multi-component rice using computer simulation
Bibliographic record
Abstract
To accommodate the varying combinations of proportions, dosages, and flavors of multi-component rice products, a previously established computer model was used to systematically analyze the influence of three types of factors (system parameter, sample structure, and material property) on the radio frequency (RF) heating characteristics in a multi-component rice sample. The results demonstrated that the highest sensitivity (84.8 %) to the volumetric heating rate ( HR ) was found with the sample height with ±20 % variation. Electrode gap, loss factor ( ε” ), and sample height all showed influences on the temperature uniformity index ( TUI ) above 30 %. In addition, a high negative correlation was noted with equal degree between HR and TUI under the variation of the sausage diameter, which was contrary to most of the other parameters. This could be attributed to the enhanced and uniform absorption of RF energy by the whole sample, resulting in a more consistent heat generation. A controllable and adjusted TUI was achieved under a relatively constant HR in simulation with the rotation of the sausage cylindrical generatrix. The combination of the high sensitivity of the sample height and the positive effects of two aforesaid types of internal component shape was conducive to the subsequent scheme design to further improve the overall heating effect. The systematic sensitivity analysis for multi-component rice in this study could provide a theoretical basis and directional guidance for parameter optimization studies in RF heating treatments of other types of heterogeneous foods. • Validated model was systematically used to analyze parameter sensitivity on RF heating. • Sample height showed highest sensitivity on heating rate and uniformity index. • A high negative correlation between HR and TUI was observed for sausage diameter. • Sensitivity analysis provided directional guidance for RF heated heterogeneous foods.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".