Modeling of Peach Drying Process using Infrared Dryer by Genetic Algorithm Method
Subject Areas : MicrobiologyF. Salehi 1 , S. H. Hosseini Ghaboos 2 *
1 - Assistant Professor of the Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran.
2 - Assistant Professor of the Department of Food Science and Engineering, Azadshahr Branch, Islamic Azad University, Azadshahr, Iran.
Keywords: Genetic algorithm, Infrared, Peach, Sensitivity analysis,
Abstract :
Introduction: Due to low energy efficiency and prolonged drying time of agricultural products by conventional methods, application of the new techniques such as infrared radiation, has been investigated. Materials and Methods: In this study, in order to dry and increase the shelf life of peach, infrared radiation (IR) method has been employed. The effect of infrared lamp power at three levels 150, 250 and 375 watts, the distance of the samples from the lamp at three levels 5, 7.5 and 10 cm and at 1 minute intervals for up to 120 minutes on drying of peach were examined. Modeling of process was carried out with genetic algorithm–artificial neural network (GA-ANN) method with 3 inputs (lamp power, distance and time) and1output (weight loss). Results: The results of infrared drying of peach showed that by increasing the lamp power and decreasing the sample distance from the heat source, the drying rate is increased. By increasing the infrared lamp power from 150 to 375 watt, the weight loss is increased from 39.4 to 87.50 % and with increase in 250 watt lamp distance from 5 to 10 cm, the weight loss is decreased from 87.6 to 73.5 % for the sample. The GA-ANN modeling results showed that a network with 13 neurons in 1 hidden layer with using hyperbolic tangent activation function can predict the weight loss in peach drying using infrared method (R=0.9991). Conclusion: Sensitivity analysis results by optimum ANN showed that drying time is the most sensitive factor to control the weight loss of peach slides.
Afzal, T., Abe, T. & Hikida, Y., (1999). Energy and quality aspects during combined FIR-convection drying of barley. Journal of Food Engineering 42(4), 177-182.
Erenturk, S. & Erenturk, K., (2007). Comparison of genetic algorithm and neural network approaches for the drying process of carrot. Journal of Food Engineering 78(3), 905-912.
Germer, S. P. M., Queiroz, M. R., Aguirre, J. M., Berbari, S. A. G. & Anjos, V. D. (2010). Process variables in the osmotic dehydration of sliced peaches. Food Science and Technology (Campinas) 30(4), 940-948.
Hebbar, H. U., Vishwanathan, K. & Ramesh, M. (2004). Development of combined infrared and hot air dryer for vegetables. Journal of Food Engineering 65(4), 557-563.
Kingsly, R. P., Goyal, R. K., Manikantan, M. R. & Ilyas, S. M. (2007). Effects of pretreatments and drying air temperature on drying behaviour of peach slice. International Journal of Food Science & Technology 42(1), 65-69.
Lertworasirikul, S. & Saetan, S. (2010). Artificial neural network modeling of mass transfer during osmotic dehydration of kaffir lime peel. Journal of Food Engineering 98(2), 214-223.
Nimmol, C. (2010). Vacuum far-infrared drying of foods and agricultural materials. The Journal of the King Mongkut’s University of Technology North Bangkok 20, 37-44.
Pan, Z., Shih, C., McHugh, T. H. & Hirschberg, E. (2008). Study of banana dehydration using sequential infrared radiation heating and freeze-drying. LWT-Food Science and Technology 41(10), 1944-1951.
Ramzi, M., Kashaninejad, M., Salehi, F., Sadeghi Mahoonak, A. R. & Ali Razavi, S. M. (2015). Modeling of rheological behavior of honey using genetic algorithm–artificial neural network and adaptive neuro-fuzzy inference system. Food Bioscience 9, 60-67.
Rastogi, N. K. (2012). Recent trends and developments in infrared heating in food processing. Critical Reviews in Food Science and Nutrition 52(9), 737-760.
Sahari, M., Souti, M. & Emam-Jomeh, Z. (2006). Improving the dehydration of dried peach by osmotic method. Journal of Food Technology 4(3), 189-193.
Salehi, F. (2019). Recent applications and potential of infrared dryer systems for drying various agricultural products: a review. International Journal of Fruit Science.
Salehi, F., Abbasi Shahkoh, Z. & Godarzi, M. (2015). Apricot Osmotic Drying Modeling Using Genetic Algorithm - Artificial Neural Network. Journal of Innovation in Food Science and Technology 7(1), 65-76.
Salehi, F., Kashaninejad, M., Akbari, E., Sobhani, S. M. & Asadi, F. (2016). Potential of Sponge Cake Making using Infrared–Hot Air Dried Carrot. Journal of Texture Studies 47(1), 34-39.
Salehi, F. & Razavi, S. M. A. (2012). Dynamic modeling of flux and total hydraulic resistance in nanofiltration treatment of regeneration waste brine using artificial neural networks. Desalination and Water Treatment 41(1-3), 95-104.
Salehi, F. & Razavi, S. M. A. (2016). Modeling of waste brine nanofiltration process using artificial neural network and adaptive neuro-fuzzy inference system. Desalination and Water Treatment 57(31), 14369-14378.
Souti Khiabani, M., Sahari, M. & Emam-Djomeh, Z. (2003). Improving the dehydration of dried peach by applying osmotic method. Iranian Journal of Agricultural Science 34(2), 283-291.
Yazdani, M., Borghaee, A. M., Rafiee, S., Minaei, S. & Beheshti, B. (2013). Mathematical and neural networks modeling of thin-layer drying of peach (Prunus persica) slices and their comparison. European Journal of Experimental Biology 3(3), 712-721.