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Modeling Selected Properties of Extruded Waxy Maize Cross‐Linked Starches with Neural Networks
Author(s) -
Ganjyal G.M.,
Hanna M.A.,
Jones D.D.
Publication year - 2003
Publication title -
journal of food science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.772
H-Index - 150
eISSN - 1750-3841
pISSN - 0022-1147
DOI - 10.1111/j.1365-2621.2003.tb09654.x
Subject(s) - artificial neural network , extrusion , biological system , process (computing) , moisture , basis (linear algebra) , product (mathematics) , process engineering , mathematics , computer science , materials science , artificial intelligence , biology , engineering , composite material , geometry , operating system
Extrusion process is described as a complex multiple input, multiple output system. Various cross‐linked waxy maize starches were extruded at initial moisture contents of 20, 24, and 28% (dry basis) and screw speeds of 110, 150, and 190 rpm. Multiple Input, Single Output neural network models were developed for predicting individual product properties, and Multiple Input, Multiple Output models were developed for predicting selected product properties at 1 time from selected input process parameters. Models varied only in the number of hidden neurons, ranging between 8 and 14. Models had R2 values of 0.89 and greater. Models explained the process and the influence of selected process parameters on selected product properties.