Healthy bread quality index based on food label information

Abstract

The label on the package shows important information such as the origin, type, price, and nutritional and energy value of the product during the purchase of the product. A decision support system is aimed at obtaining health index corresponding to energy and nutritional values with different inputs by using fuzzy logic on bread, one of the most consumed food products, in order to increase the awareness of consumers about using label information and to make a healthy and accurate evaluation among product types. The proposed model consists of two subs and one main Mamdani type fuzzy inference systems. The main fuzzy inference system has six inputs consisting of nutrient content, total fat, sugar, salt, additives, and energy. Sub-fuzzy inference systems, on the other hand, produce outputs for total fat and nutrient content inputs, where the inputs for total fat are fat and saturated fat; the inputs for nutritional content are fiber, protein, and carbohydrates. The healthy bread quality index is determined by processing the data related to these inputs on the label into the system. Label data of 54 breads packed in 18 different types sold in supermarket chains is used. Finally, the graphical user interface and sensitivity analysis of the methodology are presented.