An adaptive neuro-fuzzy model for the detection of meat spoilage using multispectral images

The use of vision technology for quality testing of food production has the obvious advantage of being able to continuously monitor a production using non-destructive methods thus increasing the quality and minimizing cost. The performance of a multispectral imaging system has been evaluated in monitoring the spoilage of minced beef stored either aerobically or under modified atmosphere packaging (MAP), at different storage temperatures (0, 5, 10, and 15 °C). The detection system explores both qualitative and quantitative information extracted from spectral data with the aid of an advanced neuro-fuzzy identification model. The proposed model constructs its initial rules by clustering while the final fuzzy rule base is determined by competitive learning. Results indicated that multispectral information could be considered as an alternative methodology for the accurate evaluation of meat spoilage.