Volume 17, Issue 100 (2020)                   FSCT 2020, 17(100): 189-200 | Back to browse issues page

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Fadaie M, Hosseini Ghaboos S H, Beheshti B. Characterization of Dried Persimmon using Infrared Dryer and Process Modeling using Genetic Algorithm-Artificial Neural Network Method. FSCT 2020; 17 (100) :189-200
URL: http://fsct.modares.ac.ir/article-7-30475-en.html
1- Department of Food Science and Technology, Science and Research Branch, Islamic Azad University, Tehran , Iran.
2- Assistant Professor, Department of Food Science and Engineering, Azadshahr Branch, Islamic Azad University, Azadshahr , Iran , hosseinighaboos@yahoo.com
3- Assistant Professor, Department of Food Science and Technology, Science and Research Branch, Islamic Azad University, Tehran , Iran.
Abstract:   (2547 Views)
Drying is one of the ways of storing of persimmon. In this study, to increasing shelf life of persimmon and producing high-quality products, infrared dryer was used and mass transfer kinetics, density, rehydration and color of samples were measured. The results showed that radiation lamp power and distance of lamp from sample had significant effect on the moisture loss kinetics and drying time (P<0.05). With increasing in radiation power, as well as reducing the distance of samples from the source of radiation, drying time decreased. The average density and rehydration for the dried samples in infrared were 639 kg /m3  and 270 %, respectively. The average calculated color changes (ΔE) for the power of 200, 300 and 400 w were 14.43, 10.09 and 20.04, respectively. The results of modeling by genetic algorithm-artificial neural network showed that artificial neural network combined with genetic algorithm provides better results and with combine them the speed of analysis and accuracy of modeling process increases. Using a network with 15 neurons in the hidden layer and using the hyperbolic tangent activation function and percentage data used to training/validation/testing equal 20/20/60 may be predicted drying kinetics of persimmon.
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Article Type: Original Research | Subject: Statistics, modeling and response levels in the food industry
Received: 2019/02/15 | Accepted: 2020/01/15 | Published: 2020/05/30

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