Food product safety is one of the most promising
areas for the application of electronic noses. The performance
of a portable electronic nose has been evaluated in monitoring
the spoilage of beef fillet stored aerobically at different storage
temperatures (0, 4, 8, 12, 16 and 20°C). This paper proposes a
fuzzy-wavelet neural network model which incorporates a
clustering pre-processing stage for the definition of fuzzy rules.
The dual purpose of the proposed modeling approach is not
only to classify beef samples in the respective quality class (i.e.
fresh, semi-fresh and spoiled), but also to predict their
associated microbiological population directly from volatile
compounds fingerprints. Comparison results indicated that the
proposed modeling scheme could be considered as a valuable
detection methodology in food microbiology