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Electrohydrodynamic (EHD) drying is a novel non-thermal drying method to dry heat-sensitive foods faster and with lower energy. Upscaling EHD drying to dry large amounts of food is the current challenge of this technology. In this regard, we quantify how successful a newly-proposed electrode configuration for EHD dryers is for drying commercial amounts of fruit for a wide range of operating conditions. To achieve this goal, we simulate an EHD dryer using physics-based modeling. The scalability was evaluated by quantifying the impact of fruit loading density in the dryer, applied voltage, and distance between electrodes on the drying time and energy consumption. Drying fruits in a commercial EHD dryer is more optimal when the dryer is densely loaded, compared to a low loading density. Loading the trays in the dryer to a capacity of 70% increased the drying time by 16%, compared to drying a few fruits widely spaced apart, but the energy consumption was 28% less. We identified the best strategy to dry a particular batch of fruit with EHD drying to achieve the fastest drying with the least energy possible. We found that it is most energy-efficient and quick to load the dryer close to its full capacity, instead of drying smaller batches in many different runs. By loading the trays in the dryer to 70% of their capacity, we could dry 7 times faster and with 11 times less energy in a single drying run instead of drying the same amount of fruit by many different runs. This study presents a key step towards upscaling EHD drying systems for the industry to dry large amounts of fruits in the shortest possible time and more energy-efficiently., Journal of Cleaner Production, 329, ISSN:0959-6526

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., Comprehensive Reviews in Food Science and Food Safety, 20 (5), ISSN:1541-4337

Automated classification of seed varieties is of paramount importance for seed producers to maintain the purity of a variety and crop yield. Traditional approaches based on computer vision and simple feature extraction could not guarantee high accuracy classification. This paper presents a new approach using a deep convolutional neural network (CNN) as a generic feature extractor. The extracted features were classified with artificial neural network (ANN), cubic support vector machine (SVM), quadratic SVM, weighted k-nearest-neighbour (kNN), boosted tree, bagged tree, and linear discriminant analysis (LDA). Models trained with CNN-extracted features demonstrated better classification accuracy of corn seed varieties than models based on only simple features. The CNN-ANN classifier showed the best performance, classifying 2250 test instances in 26.8 s with classification accuracy 98.1%, precision 98.2%, recall98.1%, and F1-score 98.1%. This study demonstrates that the CNN-ANN classifier is an efficient tool for the intelligent classification of different corn seed varieties.

Effects of convective cross-flow of air in electrohydrodynamic (EHD) drying on drying rate of 5 mm slices of champignons have been investigated. Electro-convection issued from discharge electrode (42 needles arranged into 6×7 rows with 2×2 cm spacing, 18 kV DC voltage and 3.5 cm gap) provided average ionic wind velocity of 1.0 m/s flowing perpendicularly to the surface of champignons slices, while forced air stream at atmospheric pressure 1000 kPa, superficial velocity 1.0 m/s, temperature 22-24°C, and relative humidity 25-40%, was blown parallel to the surface of champignons slices. To study interactions between forced air cross-flow and electro-convection, the experimental protocol was designed, exploring three cases in various combinations: (1) Sole EHD, (2) air cross-flow, and (3) EHD with simultaneous air cross-flow. The case # 3 was found to be the most efficient, resulting in 10.2 g/h of water evaporation whereas drying rate was 6.6 g/h (# 1) and 3.6 g/h for (# 2). Such numbers imply that these effects are additive. In some combinations the effect of air cross-flow was the same (3.6 g/g), but electro-convection was significantly suppressed to 3.2 g/h likely because air stream removed surface water, which reduced charge transfer and electro-diffusion.In trials with different initial moisture content it was found that drying kinetics followed exponential decay in the wide range of initial moisture contents from 4.9 to 12.0 g/g (db). Drying rate due to forced air convection was found to be independent of moisture content, whereas drying rate due to electro-convection significantly depended on the moisture content. For example, the EHD drying rate of fresh-cut champignons slices with initial moisture content 10.74 g/g was 0.237 g/h, while the slices after two days in the cooler (initial moisture content dropped to 4.92 g/g) it was 0.418 g/h. Also, it was found that electro-convective drying could not remove all residual water. At the end of drying the equilibrium moisture content attained 0.2 - 0.3 g/g (aw~0.3).It appears that performance of EHD drying depends also on the product porosity as water can exist as free in open pores or be trapped in closed pores. In some experiments we observed rotation of champignon slices in the plane perpendicular to ionic wind. It happened at the end of drying when slices were light enough to be lifted by electrostatic force and dragged by the vortex. This phenomenon could be attributed either to the effect of DC electric field on polarized water molecules trapped in closed pores, or it could be electrostatic effect of ionic wind on charged porous body. Also, the hypothesis that EHD has both linear and rotational (vortex) components require further investigation.

Following background to the phenomenon of electrohydrodynamics with concise review of basic features like shorter drying time, lower energy consumption and better product quality, the selected key factors affecting EHD drying are examined. These include the geometry of discharge electrodes, effects of air humidity on drying rate, depression of material temperature, and cooling effect of ionic wind. Examples are given for: (i) prototype EHD dryers of multi-belt types, and (ii) pilot-scale multi-belt EHD dryer in vertical arrangement that can be aggregated into one unit of higher capacity, and vertical cylindrical EHD dryer with vibrated shelves. Keywords: ionic wind; corona discharge; drying; energy; quality

Previous experiments have demonstrated positive effect of Acadian(®) extract of Ascophyllum nodosum on plant stress-resistance, however the mode of action is not fully understood. The aim of this study was to understand the physiological effect of Acadian(®) seaweed extract on the plant response to drought stress. Leaf temperature and leaf angle were measured as early-stage indicators of plant stress with thermal imaging "in situ" over a 5-day stress-recovery trial. The early stress-response of control became visible on the third day as a rapid wilting of leaves, accompanied with the asymptotic increase of leaf temperature on 4-5 °C to the thermal equilibrium with ambient air temperature. At the same time Acadian(®) treated plants still maintained turgor, accompanied with the linear increase in leaf temperature, which indicated better control of stomatal closure. Re-watering on the fifth day showed better survival of treated plants compared to control. This study demonstrated the ability of Acadian(®) seaweed extract to improve resistance of soybean plants to water stress.

Several experimental methods for measuring porosity, bulk density, and volume reduction during drying of foodstuffs are available. These methods include, among others, geometric dimension, volume displacement, mercury porosimeter, micro-CT, and NMR. However, data on their accuracy, sensitivity, and appropriateness are scarce. This article reviews these experimental methods, areas of applications, and limits. In addition, the concept of porosity, bulk density, and volume reduction and their evolution as a function of moisture content during drying are presented. In this study, values of initial porosity (ϵ0) and density ratio (β) of some food products are summarized. It has been found that ϵ0 is highly dependent on the type of food products, while β ranges from 1.1 to 1.6. The possibility of calculating solid density based on food compositions has also been validated. The inter-predictions between porosity, bulk density, and volume density have been made mathematically evident.