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Modeling Selected Properties of Extruded Rice Flour and Rice Starch by Neural Networks and Statistics¹

¹A contribution of the University of Nebraska Agricultural Research Division, Lincoln, NE 68583. Journal Series No.13823. This study was conducted at the Industrial Agricultural Products Center. ²MGP Ingredients, Inc., Atchison, KS 66002. ³University of Nebraska, Industrial Agricultural Products Center, 208 L.W. Chase Hall, Lincoln, NE 68583-0730. ⁴Corresponding author. Phone: 1-402-472-1634. Fax: 1-402-472-6338. E-mail: mhanna1@unl.edu ⁵University of Nebraska, Biological Systems Engineering Department, 215 L.W. Chase Hall, Lincoln, NE 68583-0730. ⁶Asian Institute of Technology, Food Engineering and Bioprocess Technology, P.O. Box 4, Pathumthani, Thailand 12120.

Rice flour and rice starch were single-screw extruded and selected product properties were determined. Neural network (NN) models were developed for prediction of individual product properties, which performed better than the regression models. Multiple input and multiple output (MIMO) models were developed to simultaneously predict five product properties or three product properties from three input parameters; they were extremely efficient in predictions with values of R² > 0.95. All models were feedforward backpropagation NN with three-layered networks with logistic activation function for the hidden layer and the output layers. Also, model parameters were very similar except for the number of neurons in the hidden layer. MIMO models for predicting product properties from three input parameters had the same architecture and parameters for both rice starch and rice flour.