Estimation of Moisture Ratio and Specific Energy Consumption for Apple Slices Drying by Convective and Microwave Methods using Neural Network Modeling

Two different drying methods were applied for dehydration of apple, i.e., convection drying (CD) and microwave drying (MD). The process of convection drying through divergent temperatures; 50, 60 and 70 °C at 1.0 m/s air velocity and three different levels of microwave power (90, 180, and 360 W) were studied. In the analysis of the performance of our approach on moisture ratio of apple slices, artificial neural networks (ANNs) was used to provide with a background for further discussion and evaluation. In order to evaluate the models mentioned in the literature, the Midilli et al. model was proper for dehydrating of apple slices in both MD and CD. The microwave drying technology enhanced the drying rate when compared with convective drying significantly. Effective diffusivity of moisture in CD drying (1.95×10−7 4.09×10−7 m/s) was found to be lower than that observed in MD (2.94×10−7–8.21×10−7 m/s). The Ea values of convective drying and microwave drying were 122.28125 kJ/mol and 14.0115.03 W/g respectively. The MD had the lowest SEC as compared to CD drying methods. According to ANN results, the best values for predication of MR in CD and MD were 0.9993 and 0.9990, respectively.


Introduction
Apple (Malus domestica Borkh.) is one of the oldest fruits known to mankind and has grown to nourish it. It is one of the most important horticultural products in the world, and countries such as China, the United States, Turkey, Poland, India, the Russian Federation and Iran are considered as major apple producers. Apples, like many other fruits, have a high water content (80-85% on w.b.).
Apple is rich in vitamins, minerals and fiber and is usually consumed raw, but it is used in many foods (especially desserts) and beverages 1,2,3 . Drying, in addition to being a way to increase the shelf life of foods, is known as a way to increase the value added of food products. Removing water from a product under controlled conditions reduces the moisture content of the food to a certain extent, which lessens the activity of enzymes, the rate of undesirable chemical changes and microbial growth. Also, the decrease in moisture is accompanied by a reduction in volume and weight, which is one of the important factors for transportation and maintenance 4 . Throughout the decades, hot air drying method has been one of the most long-established technologies in the food industries. The process of hot air drying includes both the heat and mass transfer while the water is provided by the agricultural products through diffusion. However the total energy of this diffusion goes hand in hand with air temperature, time and air velocity 5 . One of the methods that has been given a lot of attention during the last decade is drying using microwave radiation. Microwave beams are electromagnetic beams with a long wavelength of 2450 MHz. During the passing of these waves from the tissue of matter, polar molecules, such as water and salts, vibrate, and this vibration causes the microwave energy to be converted into heat. Unlike other methods of drying, in which heat should penetrate from the surface to depth, in this method heat is produced in the tissue of the food itself and it is prevented from damaging the superficial parts of the food 6,7 . Different methods are used to reduce the moisture content of fruits and vegetables. Izli and Isik 8 used microwave, convective, and microwave-convective dryers to dry tomatoes. They showed that microwave-convective dryers require less time to dry tomatoes.
Seremet et al. 9 investigated effect of different drying methods (Hot air convection and hot air convection-microwave dryer) on weight loss and rehydration of sliced pumpkin. Drying of sorbus fruits by convective (50°C and 70°C at air velocity of 0.3 m/s) and microwave (90, 160 and 350 W) were studied in order to determine the drying behaviors. The results showed that the temperature of 50 °C and the microwave power of 90 W had the slightest variations in color. Also, the lowest specific energy consumption were 0.69 kWh/kg and 37.07 kWh/kg respectively at 70°C and 350 W 10 . The correlation of the unpredictable input and output process parameters interconnection follows the stimulated computing approach named Artificial Neural Network (ANN) 11 . ANNs are capable of modeling nonlinear and complex systems with a large number of input and output data. The ability to predict a neural network is completely dependent on its structure (type of activation function, number of layers and number of hidden layer neurons) 12.13 . In recent years, methods based on ANNs have been used to predict the moisture content of many food and agriculture products during the drying process, including green peas, tomatoes, corn and pomegranate seeds [14][15][16][17] . In this research, the neural network modeling method was used to estimate the moisture ratio of apple slices during drying in microwave and hot air dryer. The results of this model are compared with the results of mathematical modeling to determine its effectiveness. Also, moisture diffusion coefficient, activation energy, specific energy consumption and color changes were also determined for apple slices.

Sample preparation
Apple was supplied from one of apple orchards of Ardabil city, Iran, in September 2016. Generally, apple samples of uniform sizes were selected. The apple fruit were cleaned and stored in a refrigerator at 4±1 o C. The premature and spoiled apple was separated manually. The initial MC of apple slices was measured by oven drying method. Apple slices to the nearest 40 g (4 mm thickness and 36 mm diameter) in triplicate samples were dehydrated at 70±1 o C for 24 h 18 . Apple fruit with average initial MC of 45% (d.b.) was selected for drying material.

Convective dryer
Convective drying (CD) was conducted by using laboratory drying oven (BF55E; FG Co., Iran). The velocity of the air approaching to the apple samples was measured by an anemometer (Lutron AM-4202; Electronic Enterprise Co., Taipei, Taiwan) with ±0.1 m/s accuracy and the average air velocity was 1.2 ± 0.02 m/s. Electrical heating unit of this dryer equipped with PT100 thermometer sensor and PID controller with ±0.1C accuracy. Average humidity and air temperature of ambient air during convection dryer were 30% and 26°C, respectively.

Mathematical modelling of drying curves
The models listed in Table 1

Models Equation References
Midlli et al.

Effective moisture diffusivity
Mass transfer during food drying is a complex process involving various mechanisms such as molecular penetration, movement in capillary tubes, and liquid penetration in the porous materials, penetration of vapor in air pores and hydrodynamic flow, or surface propagation. Moisture penetration is one of the most important factors controlling the drying process. When different mechanisms are effective in transmitting, it is difficult to examine each mechanism and measure the mass transfer rate in each one. Hence, in such processes, the description of effective diffusion is used and its concept is described by the Fik's second law as follows 30 : Calculation of effective diffusion coefficient using the Fik's second law is a tool for describing the drying process and possible mechanisms for the transfer of moisture within food products. The analytical solution of Fik's law is as follows 31 : Where i is a positive integer that is equal to 1 for long drying time. Therefore, Equation 9 can be written in simpler form as Equation 10: The coefficient 1 K is calculated by plotting the curve ln(MR) versus time, in accordance with Equation 11 as follows 32 :

Activation energy
Dependence of the diffusion coefficient with temperature is shown using the Arrhenius equation (Equation 12). Activation energy of the convective dryer ( ) (c a E ) was determined by plotting the effective moisture diffusion coefficient curve versus absolute air temperature reversal 33 .
The linear form of Equation 12 can be obtained by applying the logarithms as: Linear regression analyses were used to fit the equation to the experimental data to obtain correlation The activation energy for microwave dryer ( ) (m a E (W/g)) was calculated by using a correlation between effective moisture diffusivity and ( P m ) is taken into account 34 : (15) ) (m a E may be accomplished using one of several methods as follows: 3 K is calculated for the microwave as follows:

Specific energy consumption
The specific energy consumed during the drying process, which is the amount of energy used to evaporate one kilogram of water from the product, was obtained using

Color
Three color schemes, including RGB, CMYK and Lab, are used to determine the color of food. The Lab model is often used for food color research studies. L demonstrates brightness in the range 0-100, and two colored components (-120 -+120) including a (greenness to redness) and b (blueness to yellowness). The color parameters of apple slice were measured using digital portable colorimeter (CR-10-PLUS, Konica Minolta Co, Japan), appropriate test method based on CIELAB. Total color changes ( E  ) was calculated using Equation 20. All color changes were obtained with averaging in six replicates samples 38,39 :

ANN
ANN was used for modeling the drying process of apple slice in microwave and hot air dryer to predict MR by using Matlab software. In this research, the Levenberg-Marquard optimization method was used to learn the network. The inputs for ANN model are drying time, and drying chamber inlet air temperature, and the output is MC variations of apple slice. Figure 1 shows ANN inputs and output structure with two hidden layers. were derived. Networks with two neurons in the input layer (air temperature and drying time) and one 9 neuron in the output layer (MR) were designed. In this part, the total data of, moisture ratio (163 data) for artificial neural networks were used. In the first group, 70% (115 data) were taken for training phase and in the second group 30% (48 data) for testing, chosen randomly from the set of 163 data.

2.3.7.2.Microwave dryer
Applying the two inputs in all experiments, the moisture ratio values obtained for different conditions. Networks with two neurons in input layer (microwave power and drying time) and one neuron in output layer (MR) were designed. About 70% (49 data) of the all experimental data (70 data) were separated for network training to find suitable structure. Prior to training the neural network, input data normalized to it. The purpose of normalizing is to convert data between zero and one. Therefore, the following equation was used for normalization 40   The results showed that with increasing microwave power, the drying time had a downward trend.
Similar results were obtained for drying crops in a microwave dryer such as pomegranate arils 48 , mushroom, tomatoes 8 and broccoli stalk slice 49 .
In order to mathematical modeling of apple slice drying kinetics in the convective dryer, five commonly mathematical models for thin layer products were used (  The results of the fitting of apple slices drying data in microwave method with different mathematical models were presented in Table 3.

Activation energy
During the drying process, the highest values of activation energy for CD and MD methods were obtained 125 kJ/mol and 15.03 W/g, respectively ( Table 4 Figure 4a shows the specific energy consumption of drying process of apple slice in convection dryer.

Specific energy consumption (convection and microwave)
In this study, the specific energy consumption was obtained in the range of 122.77 to 174.67 MJ/kg.
According to the results, the highest and lowest energy values were consumed in the process of drying apple slices at 50 and 70°C, respectively. As shown in Figure

Color for (convection and microwave)
Color is one of the most important qualitative properties of fresh, processed food and its marketing. As shown in Figure 63 , the moisture ratio of zucchini were predicted by using artificial neural networks at convective dryer. According to the results, the coefficient of determination 0.998 and the RMSE value (0.0335) for the moisture ratio was obtained.  (Table 6).