Fuzzy Classification Approach on Quality Deterioration Assessment of Tomato Puree in Aerobic Storage using Electronic Nose

Abstract

Food safety heavily deals with food spoilage that may yield food poisoning. Tomato-based dishes have different shelf-life leading to unique acceptable standards for a person in determining the food condition, and sometimes misclassification due to confusion. To address this problem, a proposed solution is the development of an intelligent electronic nose (eNose) system that will discriminate the condition of tomato puree using fuzzy logic. This system is composed of two sections: the development of electronic nose using Gizduino microcontroller and Mĭngan Qǐ lai (MQ) gas sensors, and the implementation of fuzzy logic system for classification of food condition. Fuzzy logic resembles human reasoning that yields definite output based on ambiguous input. The collection data rate was set to 2 Hz for tomato puree-emitted gas samples with varying shelf life considering outdoor aerobic storage. Combined Min-Max method and Mamdani inference system was used for the inference engine, and centroid method for defuzzification. The system classifies the tomato puree sample as not spoiled, partially spoiled, and spoiled. The smellprint of each food condition was generated and the tomato puree-spoilage determinant parameters were characterized. Through embedded fuzzy logic, an accuracy of 90.00 % was yielded for tomato puree quality deterioration classification. The developed mechanism is a potential application in domotics.