Applying Disciminant Analysis and D.Abased Artificial Neural Network to investigate discriminators of high and middle Waste Bakers and forecasting their categories (case of Mashhad)

This study contributes to reduce bread waste in the production process by determining effective factors that distinguish high bread wast bakers from low bread waste bakers using 250 bakeries over Mashhad in the year 2010. The discriminant analysis was used to predict the study bakers into one class of high or low waste groups. Results indicate that among discriminators, bakery status, bread waste price, dough fermentation time, daily consumption of flour, quality of produced bread, percentage of wet gluten, moisture percentage, quality of flour and maintenance costs have the highest share in distingushing between high and low bread waste bakers. Predicting bakers based on their bread waste was considered as a suitable instrument in order to identify effective actions for reducing bread waste. In this study, classification accuracy of discriminant analysis (DA) and DA-based artificial neural network indicates high accuracy of class prediction at training and testing data with DA-based artificial neural network model. Ultimately, based on the results, a number of applicable and executive recommendations to decrese bread waste in the production process were presented.