Wheat Flour Milling Yield Estimation Based on Wheat Kernel Physical Properties Using Artificial Neural Networks

Abstract

Wheat is a stable food raw material for the majority of people around the world as wheat-based products provide an important part of the daily energy intake in many countries. Wheat is generally milled into flour prior to use in the bakery industry. Flour yield is one of the major quality criteria in wheat milling. Flout yield detection requires large amount of sample, costly machinery, longer time of tempering and milling practices requiring substantial workload. In this study, artificial neural network (ANN) approach has been employed to predict flour milling yield. The ANN was designed in the MATLAB software using such wheat physical properties as hectoliter weight, thousand-kernel weight, kernel size distribution, and grain hardness. Flour yields and four different kernel physical features (hectoliter weight, thousand-kernel weight, kernel size distribution, and grain hardness) were first collected from 2400 wheat samples through the conventional methods. The ANN was trained using 85% of 2400 flour yield data and tested with 15% of the remaining data. In the training of the ANN, various models have been investigated to find the best ANN structure. Additionally, two data sets with and without grain hardness have been employed to determine the effect of grain hardness on the prediction performance of the ANN model. It was found that grain hardness which reduced the MAE values from 2.3333 to 2.2611 and RMSE values from 3.0775 to 2.9146 gave better result. The results proved that the developed ANN model can be used to estimate flour yield using wheat kernel physical properties.