A Rapid Detection of Meat Spoilage using an Electronic Nose and Fuzzy-Wavelet systems

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. To address the detection of meat spoilage micro-organisms during aerobic or modified atmosphere storage, an electronic nose with the aid of fuzzy wavelet network has been considered in this research. A clustering component, that could be utilized to identify fuzzy rules, has been applied initially. The proposed MIMO identification scheme classifies meat samples in their own quality class (i.e. fresh, semi-fresh, spoiled) and provides a prediction of microbiological total viable counts by analyzing volatile information from electronic nose. Results were compared against those produced by neural networks and neurofuzzy systems and indicated that the proposed method could be used as a reliable scheme for meat spoilage detection.