Paper Info
Title
A fuzzy-wavelet neural network model for the detection of meat spoilage using an electronic nose
Abstract
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. 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.
Year
DOI
Venue
2016
10.1109/FUZZ-IEEE.2016.7737757
2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Keywords
Field
DocType
fuzzy systems,wavelet,neural networks,meat spoilage,clustering
Food spoilage,Electronic nose,Population,Data mining,Meat spoilage,Computer science,Fuzzy logic,Robustness (computer science),Artificial neural network,Cluster analysis
Conference
ISSN
ISBN
Citations 
1544-5615
978-1-5090-0627-4
2
PageRank 
References 
Authors
0.51
3
2
Name
Order
Citations
PageRank
Vassilis S. Kodogiannis127235.17
Abeer Alshejari281.73