Estimation Equilibrium Moisture Content in Agriculture Product Using Neural Network Method
Journal Title: International Research Journal of Applied and Basic Sciences - Year 2012, Vol 3, Issue 11
Abstract
The equilibrium moisture content (EMC) is an important parameter for several postharvesting operations such as processing, packaging storage and drying processes. EMC is affected by different parameters Such as temperature , relative humidity, type of the product and ingredient of them. Empirical models and Artificial Neural Networks (ANNs) were utilized for the prediction of Equilibrium Moisture Content (EMC) in agriculture product. Many empirical models including GAB, Smith, Henderson, Chung-Post, Modified Halsey, Chen-Clayton, Oswin, Halsey and D’Arsy-watt, Vemuganti were applied for this estimation. Artificial neural networks (ANNs) method is the approach used in this paper to estimate EMC too. A Generalized Regression Neural Network (GRRN) was used to achieve the better results. Comparing the data obtained using this model with Vemuganti model showed the desirable result. The accuracy of the predicted rates for EMC was 92.6% to 99.7%.
Authors and Affiliations
Mohammad shekofteh| Islamic Azad University, Jiroft Branch, Jiroft, Iran,moh_shekofteh1@yahoo.com, Hosein shekofteh| Islamic Azad University, Jiroft Branch, Jiroft, Iran, Mohammad raza hojati| Junior College of Fasa, Iran
Keywords
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