An artificial neural network for prediction of Zeleny sedimentation volume of wheat flour. | [International Journal of Agriculture and Biology • 2008]

Author(s):

1. Elhame Razmi-Rad: Food Science and Engineering Group, Sarab Azad University, Sarab, Iran

2. Babak Ghanbarzadeh: Food Science and Engineering Group, Sarab Azad University, Sarab, Iran

3. J. Rashmekarim: Department of Food Science and Technology, Faculty of Agriculture, University of Tabriz, P.O. Box 51666-16471, Tabriz, Iran, Research Center of Iran Seed and Plant Improvements Institute, Tabriz, Iran

Abstract:

The sedimentation volume of gluten in flour dispersion indicates its gluten quality and thus the bread making quality of the flour. Artificial neural network (ANN) modeling was used to model the sedimentation property of wheat flour as a function of total protein, wet gluten and hardness index. In developing the ANN model, several configurations were evaluated. The optimal ANN configuration was found to be a network consisting of one hidden layer with nine neurons. The number of iterations, learning rate and momentum coefficient had important effects on the optimal configuration. The optimal model was capable of predicting the Zeleny sedimentation value with a mean absolute error (MAE) of 3.922 and root mean square error (RMSE) of 4.561.

Page(s): 422-426

DOI: DOI not available

Published: Journal: International Journal of Agriculture and Biology, Volume: 10, Issue: 4, Year: 2008

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