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Prediction of Extrudate Properties Using Artificial Neural Networks

Paper ID Volume ID Publish Year Pages File Format Full-Text
19569 43076 2007 5 PDF Available
Title
Prediction of Extrudate Properties Using Artificial Neural Networks
Abstract

A backpropagation artificial neural network (ANN) model was developed to predict the properties of extrudates generated by extrusion cooking of fish muscle-rice flour blend in a single screw extruder. Experimental data obtained in a previous study on extrudate properties of expansion ratio, bulk density and hardness at different combinations of operating variables of barrel temperature, feed content and feed moisture had been analysed using response surface methodology (RSM). A backpropagation neural network model was implemented in MATLAB and was trained for operating variables (inputs) and for each individual measured extrudate properties expansion ratio ER, bulk density BD and harndess H (outputs). The optimized network indicated that one hidden layer with a learning rate of 0.1, steep descent learning rule, 100 000 epochs and a logistic sigmoid transfer function predicted the extrudate properties better than RSM. The agreement of the ANN model with the experimental values, expressed as sum of squared error values, was 9.8 × 10−7 for ER, 5.8 × 10−2 for BD and 3.8 × 10−3 for H. The ANN prediction for the optimized process conditions was superior to the RSM values, with percentage errors of +6.06% (ER), +4.08% (BD) and −14.28% (H).

Keywords
extrusion cooking; prediction; extrudate properties; artificial neural network (ANN)
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Publisher
Database: Elsevier - ScienceDirect
Journal: Food and Bioproducts Processing - Volume 85, Issue 1, March 2007, Pages 29–33
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us