Electrohydrodynamic drying: Can we scale‐up the technology to make dried fruits and vegetables more nutritious and appealing?
Bibliographic record
Abstract
Electrohydrodynamic (EHD) drying is a promising technology to better preserve the nutritional content and sensory appeal of dried fruits and vegetables. To successfully scale up this technology, we need to rethink the current EHD dryer designs. There is also a significant potential to further enhance the nutritional content and sensory quality of the dried products by optimizing EHD process parameters. This study particularly highlights the current bottlenecks in scaling up the technology and improving nutrient retention and sensory appeal of the dried products. We discuss plausible future pathways to further develop the technology to produce highly nutritious dried products. Particular emphasis has been given to quantifying the residual nutritional and sensory properties of EHD dried products, and possible EHD dryer configurations for farmers and the industry. Concerning the nutritional content, EHD drying preserves vitamins, carotenes, and antioxidants significantly better than convective air drying. From the sensory perspective, EHD drying enhances the color of dried products, as well as their general appearance. With respect to scalability, placing the fruit on a grounded mesh electrode dries the fruit much faster and more uniformly than the grounded plate electrode. Future research should be directed toward simultaneous measurements of multiple food nutrients and sensory properties during EHD drying with a grounded mesh collector. Quantifying the impact of the food loading density on drying kinetics and energy consumption of the EHD drying process should also be a future research goal. Research comparing EHD drying with commercially available drying methods such as freeze-drying, microwave-drying, and infrared drying should also be carried out. This study gives promising insight toward developing a scalable novel thermal drying technology tailored to the requirements of the current and future society.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".