The Effect of Material Thickness, Load Density, External Airflow, and Relative Humidity on the Drying Efficiency and Quality of EHD-Dried Apples
Bibliographic record
Abstract
Electrohydrodynamic drying is a novel non-thermal technique for dehydrating heat-sensitive foods. However, its industrial applications are limited due to the underexplored effects of material properties and environmental conditions on product quality. For this purpose, a multifactorial experiment was designed to study the effects of material thickness, load density, external airflow, and humidity on the EHD drying efficiency and quality of apple slices. The experiments show that the intensity of EHD drying increased with a decrease in humidity, slice thickness, and load density. The effective diffusivity of apple slices with EHD drying was about 5.17·10−12 m2/s, slightly increasing with external airflow. The specific energy consumption of EHD drying was 10–12 times lower than hot air drying. The time of EHD drying at 20 °C was equivalent to hot air drying at 40 °C, but the impact of EHD drying on the product quality was significantly lower. EHD drying better preserved the color and phenolic content in dried apple slices, with less cellular damage. Hence, EHD drying can be employed in industry as a sustainable alternative to hot air drying.
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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 | 0.001 |
| 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".