Food Engineering
Fakhreddin Salehi; Moein Inanloodoghouz; Sara Ghazvineh; Parisa Moradkhani
Abstract
IntroductionSour cherries (Prunus cerasus L.) are relatively diverse and broadly distributed around the world, being found in Asia, Europe, and North America. Sour cherries have unique anthocyanin content, and rich in phenolic compounds. The fruits are generally used for processing purposes, such as ...
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IntroductionSour cherries (Prunus cerasus L.) are relatively diverse and broadly distributed around the world, being found in Asia, Europe, and North America. Sour cherries have unique anthocyanin content, and rich in phenolic compounds. The fruits are generally used for processing purposes, such as for production juice and jam. The fruits of sour cherries can also be frozen and dried. One of the best methods for the preservation of agricultural product is drying, which involves removing water from the manufactured goods. Dried sour cherries have a long shelf life and therefore may be a fine alternative to fresh fruit all year round. There are no reports on the effect of microwave pretreatment on the hot air drying kinetics of sour cherries in the literature. Hence, the purpose of this study was to estimate the impacts of microwave pretreatment on the total phenolics, drying time, mass transfer kinetic, effective moisture diffusivity, total color difference index, shrinkage and rehydration of sour cherry. In addition, the moisture ratio changes of sour cherry during drying were modeled. Material and MethodsSour cherries were purchased from the market at Bahar, Hamedan Province, Iran. The average diameter of fresh sour cherries was 1.6 cm. In this study, the water content of fresh and dried sour cherries was calculated using an oven at 103°C for 5 h (Shimaz, Iran). In this research, the effect of microwave time on the drying time, effective moisture diffusivity coefficient and rehydration of sour cherries was investigated and drying kinetics were modeled. To apply the microwave pretreatment on the sour cherries, a microwave oven (Gplus, Model; GMW-M425S.MIS00, Goldiran Industries Co., Iran) was used under atmospheric pressure. In this work, the influence of the microwave pretreatment time at five levels of 0, 30, 60, 90, and 120 s (power=220W) on the cherries was examined. After taking out the treated sour cherries from microwave device, the samples were placed in the hot-air dryer (70°C) as a thin layers. The dehydration kinetics of sour cherries were explained using 7 simplified drying equations. Fick's second law of diffusion using spherical coordinates was used to calculate the moisture diffusivity of sour cherries at various hot-air drying conditions. The rehydration test was conducted with a water bath (R.J42, Pars Azma Co., Iran). Dried sour cherries were weighed and immersed for 30 min in distilled water in a 250 ml glass beaker at 50°C. Results and DiscussionThe results showed that microwave treatment led to an increase in moisture removal rate from the sour cherries, an increase in the effective moisture diffusivity coefficient, and, consequently, a decrease in drying time. By increasing the microwave time from 0 to 12 s, the average drying time of sour cherries in the hot-air dryer was decreased from 370 min to 250 min (p<0.05). The average effective moisture diffusivity coefficient calculated for the samples placed in the hot-air dryer was 4.25×10-10 m2/s. Increasing the microwave time from 0 to 120 s increased the average effective moisture diffusivity coefficient by 85%. The maximum amount of phenolic was related to the sample treated with microwave for 90 seconds. Microwave treatment time had no significant effect on the rehydration of dried sour cherries. ConclusionKinetic modeling of weight changes of sour cherries during drying was carried out using models in the sources, followed the Page model was selected as the best model to predict moisture ratio changes under the selected experimental conditions. The mean values of sum of squares due to error, root mean square error, and r for all samples ranged from 0.001 to 0.007, 0.005 to 0.017, and 0.997 to 0.999, respectively. Generally, 120 s pre-treatment by microwave is the best condition for drying sour cherries.
Mohammad Ebrahim Mohammadpour Mir; Sara Nanvakenari; Kamyar Movagharnejad
Abstract
Introduction: Rice is one of the most important cereals and is the second-highest worldwide production after wheat and also is a good source of nutrients for humans. It plays an important role in the feeding of the many parts of the world including Iran. The harvested paddy rice has the high initial ...
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Introduction: Rice is one of the most important cereals and is the second-highest worldwide production after wheat and also is a good source of nutrients for humans. It plays an important role in the feeding of the many parts of the world including Iran. The harvested paddy rice has the high initial moisture content of nearly 25-28% (wet basis) that caused corruption. Therefore, in order to prevent corruption and safe storage, it must be dried to 10-13% moisture content. Drying is one of the oldest methods of preserving food and agricultural products that used to increase the food’s storage time. There are several methods for drying paddy rice that none of them are ideal and have several advantages and disadvantages that one of them that recently the use of it has been increased is microwave drying. Microwave drying uses electromagnetic radiations with the frequency range of 300 MHz to 300 GHz and the wavelengths of 1-0.01m. In microwave drying due to better energy concentration, moisture is removed more quickly so the drying time decreases. Due to the complex relationship between input and output variables in the drying process, the selection of the model that can estimate the drying behavior of the products is difficult. Hence, the use of intelligent modeling methods such as neural networks is the best choice. Materials and methods: In this research, in order to investigate the effect of microwave power on kinetics of rice drying, head rice yield and effective diffusivity coefficient of moisture, a continuous type of domestic microwave dryer ( DEM-281 QOT-PW) were used. This dryer has a microwave radiation chamber where the samples are put on it on the tray that was placed on a digital balance. The experiments were performed at three microwave power levels designated as 270, 360 and 450 W. Also, Shirudi paddy rice was used as the raw material and the drying rice process from the initial moisture content of 21% to the final moisture content of 11% is examined. In this study, the neural network toolbox of MATLAB 2017R was used to model the kinetics of rice drying in the microwave dryers. RBF and MLP have 3 layers including input, hidden and output layers. The input layer has two neurons that show the number of input variables that were time and microwave power and the output layer has one neuron that shows the number of output variables that was MR in this study. 70% and 30% of the data was used for training and testing the network, respectively. To estimate the ANN performance, mean square error (MSE) and the coefficient of determination (R2) was used. Results and discussion: The maximum and minimum drying time was 42 and 20 minutes in 270 and 450 watts, respectively. Also, the maximum and minimum effective diffusivity coefficient of moisture were 4.17 * 10^-9 and 1.82* 10^-9 in 450 and 270 watts, respectively. RBF network with Guassian transfer function and high neurons number and MLP network with Levenberg-Marquardt ( LM) learning algorithm and tan-sigmoid (tansig) transfer function with low neurons number were able to model the kinetics of drying as well as. In general, the drying time and head rice yield decreased but the effective diffusivity coefficient of moisture increased by increasing the microwave power. Also drying at different microwave power did not affect rice color and quality. The results of the modeling of rice drying by using two different neural networks including MLP and RBF demonstrated that the MLP network with Levenberg-Mrrqurdt (LM) learning algorithm and tan-sigmoid (tansig) transfer function has the better performance than the RBF network with Gussian transfer function and the error and the correlation coefficient in MLP are less and higher than the RBF, respectively.