Neural network based identification of meat spoilage using Fourier-transform infrared spectra

To address the rapid and non-destructive detection of meat spoilage microorganisms during aerobic storage at chill and abuse temperatures, Fourier transform infrared spectroscopy (FTIR) with the help of an intelligent-based identification system was attempted in this work. The objective of this study is to associate simultaneously spectral data with microbiological data (log counts), for Total Viable Counts, Pseudomonas spp., Brochothrix thermosphacta, Lactic Acid Bacteria and Enterobacteriaceae. The dual purpose of the proposed modelling scheme is not only to classify meat samples in the respective quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from FTIR spectra. An Extended Normalised Radial Basis Function neural network has been implemented, and the Bayesian Ying-Yang Expectation Maximisation algorithm has been utilised together with novel splitting operations to determine network’s size and parameter set. The dimensionality reduction of spectral data has been addressed by the implementation of a fuzzy principal component algorithm. Results confirmed the superiority of the adopted methodology compared to other schemes such as multilayer perceptron and the partial least squares techniques and indicated that spectral information obtained by FTIR spectroscopy during beef spoilage, in combination with an efficient choice of a learning-based modelling scheme could be considered as an alternative methodology for the accurate evaluation of meat spoilage.