A rapid detection of meat spoilage using FTIR and neuro-fuzzy systems

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. To address the rapid detection of meat spoilage microorganisms during aerobic storage at chill and abuse temperatures, Fourier transform infrared spectroscopy with the aid of a neuro-fuzzy identification model has been considered in this research. Spectral information was obtained from the surface of beef samples during aerobic storage at various temperatures, while a microbiological analysis had identified the population of Total Viable Counts. The intelligent model constructs its initial rules by clustering while the final fuzzy rule base is determined by competitive learning. Results confirmed the advantage of the proposed scheme against the adaptive neuro-fuzzy inference system and multilayer perceptron in terms of prediction accuracy.