Artificial Neural Network and Their Applications in Food Materials: A Review
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This paper is a review of artificial neural network technique for the prediction of drying parameters of food materials. The meaning of ANN, the importance, areas that ANN could be applied, future prospects and summary of previous researchers work using ANN for the prediction of drying parameters were considered. These drying parameters are not limited to the following: thickness, temperature, velocity, moisture content, drying rate that are used in the prediction. Thus, ANNs hold a great deal of promise for modeling complex tasks in process control and simulation and in applications for food safety, preservation and quality control. This method eliminates the need for manual calculations and the ANN representing more tools for prediction drying parameters of food materials. This technique is preferred for large data set for robust, accuracy and less time consuming benefits. The method/leaning algorithm mostly used was Levenberg-Marquardt back propagation and the coefficient of determination (R2) was above 0.9 and the moisture content was one of the key output parameter that was determined.
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