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

Authors

G. Ganjyal, MGP Ingredients, Inc., Atchison, KS
Milford A. Hanna, University of Nebraska - LincolnFollow
P. Supprung, Thailand
A. Noomhorm, Thailand
David D. Jones, University of Nebraska - LincolnFollow

Date of this Version

2006

Comments

Published in Cereal Chem. 83(3):223–227. Copyright © 2006 AACC International, Inc. Used by permission.

Abstract

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 feed-forward 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.