DisplayTitle

Neural Network Modeling of Physical Properties of Ground Wheat¹

¹Journal Series No. 11818, Agricultural Research Division, Institute of Agriculture and Natural Resources, University of Nebraska-Lincoln. ²Industrial Agricultural Products Center, University of Nebraska, Lincoln, NE 68583-0730. ³Corresponding author. E-mail: qfang@unlgrad1.unl.edu ⁴Dept. Grain Science and Industry, Kansas State University, Manhattan, KS 66506. ⁵Dept. Biological and Agricultural Engineering, Kansas State University, Manhattan, KS 66506.

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 R² (>0.98). The neural network models are, therefore, capable of predicting the physical properties of the ground wheat.