Publication no. C-1998-0212-01R |  VIEW ARTICLE

Neural Network Modeling of Physical Properties of Ground Wheat (1).

Qi Fang (2,3), Gerald Biby (2), Ekramul Haque (4), Milford A. Hanna (2), and Charles K. Spillman (5). (1) Journal Series No. 11818, Agricultural Research Division, Institute of Agriculture and Natural Resources, University of Nebraska-Lincoln. (2) Industrial Agricultural Products Center, University of Nebraska, Lincoln, NE 68583-0730. (3) Corresponding author. E-mail: <qfang@unlgrad1.unl.edu> (4) Dept. Grain Science and Industry, Kansas State University, Manhattan, KS 66506. (5) Dept. Biological and Agricultural Engineering, Kansas State University, Manhattan, KS 66506. Cereal Chem. 75(2):251-253. Accepted December 16, 1997. Copyright 1998 by the American Association of Cereal Chemists, Inc.

Physical properties of ground materials from roller mills are affected by the characteristics of wheat and the operational parameters of the roller mill. Backpropagation neural networks were designed, trained, and tested for the prediction of three physical properties of ground wheat: geometric mean diameter (GMD), specific surface area increase (SSAI), and break release (BR). Eight independent variables were used as input data. Compared to conventional statistical models, the accuracy of prediction was improved substantially, as reflected by the significant reduction in root mean squared error (RMS), relative error (RE), and the increase in coefficient of determination R(^2) (>0.98). The neural network models are, therefore, capable of predicting the physical properties of the ground wheat.