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Modeling Selected Properties of Extruded Rice Flour and Rice Starch by Neural Networks and Statistics
Author(s) -
Ganjyal G.,
Hanna M. A.,
Supprung P.,
Noomhorm A.,
Jones D.
Publication year - 2006
Publication title -
cereal chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.558
H-Index - 100
eISSN - 1943-3638
pISSN - 0009-0352
DOI - 10.1094/cc-83-0223
Subject(s) - backpropagation , artificial neural network , biological system , feedforward neural network , starch , product (mathematics) , rice flour , feed forward , logistic function , layer (electronics) , mathematics , food science , chemistry , statistics , computer science , raw material , artificial intelligence , engineering , biology , geometry , organic chemistry , control engineering
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 R 2 > 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.