Electrohydrodynamic drying of fruit slices: Effect on drying kinetics, energy consumption, and product quality
Bibliographic record
Abstract
The effect of electrohydrodynamic drying on the drying kinetics, energy consumption, and quality was studied for apple and strawberry slices with different thicknesses from 1 to 4 mm. Effective diffusivity and energy consumption were quantified via direct measurements, whereas visual quality attributes, such as color and shrinkage, were determined through image analysis. It was found that effective diffusivity was independent of thickness, around 0.5 × 10−11 m2/s for apples and 0.24 × 10−11 m2/s for strawberries. The average specific energy consumption increased with the fruit thickness from 601.13 kJ/kg (1 mm) to 1433.83 kJ/kg (4 mm) for apple slices and from 1324.17 (3 mm) to 1756.71 kJ/kg (4 mm) for strawberry slices. The effect of EHD drying on apple color was significantly smaller than conventional hot air drying. The impact of EHD drying on the shrinkage of apple slices was about 85%, and for strawberry slices, about 90%, which was higher than in hot air drying. It can be concluded that EHD is effective for drying thin fruit slices due to the low energy consumption and better quality. Novelty impact statement The main advantage of EHD drying over conventional drying techniques is its low energy consumption and better product quality, which is beneficial for the commercialization of the technique. In this study, the effect of fruit thickness on drying kinetics, energy consumption, and product quality were evaluated for apple and strawberry slices. The specific energy consumption increased with thickness and was very low compared to thermal drying. EHD dried fruit slices had better visual quality than thermal 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) | 0.001 | 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".