جستجوی مقالات مرتبط با کلیدواژه "microwave dryer" در نشریات گروه "صنایع غذایی"

Evaluation of the effect of ohmic pretreatments and blanching (enzyme removal) on changes in the biochemical properties of carrots was the purpose of this research in the drying process using the response surface method. Sliced carrot samples were added and left at 3, 5, and 7 minutes and 40, 60, and 80 volts. In the blanching pre-treatment, carrot samples were subjected to hot air from boiling distilled water for 5, 10, and 15 minutes to complete the pre-treatment process. This study dried slices of carrot samples at 360, 600, and 900 W using a microwave dryer. After drying, the amounts of antioxidant, total phenol, and flavonoid contents were determined. Then the results were analyzed using response surface methodology. The findings revealed that the high voltage and time in the ohmic pretreatment, as well as the time in the blanching pretreatment, had the lowest decreasing antioxidant content during the drying process. The lowest concentration of antioxidants was at the lowest voltage and time. Increasing the microwave power reduced the content of antioxidants, and lower drying powers had a higher antioxidant content in the dried samples. For the total phenol and flavonoid content in the ohmic pretreatment, the time and voltage of the pretreatment decreased their content, and increased power caused more total phenol and flavonoid content to be retained in the samples. Also, based on the optimization performed, the best microwave power was 360, 40 V ohmic voltage, and 3 minutes.

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.

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.

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.