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Food and Bioprocess Technology

, Volume 10, Issue 4, pp 730–749 | Cite as

Application of an Electronic Nose Coupled with Fuzzy-Wavelet Network for the Detection of Meat Spoilage

Original Paper

Abstract

Food product safety is one of the most promising areas for the application of electronic noses. Their application in this domain is mainly focused on quality control, freshness evaluation, shelf-life analysis and authenticity assessment. In this paper, the performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillets stored either aerobically or under modified atmosphere packaging, at different storage temperatures. A novel multi-output fuzzy wavelet neural network architecture has been developed, which incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modelling approach is not only to classify beef samples in the relevant quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population, based on total viable counts. For the case of aerobic packaging, model was able to classify correctly 67 out of 70 aerobic samples (95.71%), while successful identification of microbial counts resulted in a 4.57% standard error of prediction. However, under modified atmosphere packaging scenario, results were rather inferior, as proposed model achieved a 92.95% classification rate (66 out of 71 samples), while the standard error of prediction of microbial counts was increased to 5.74%. In comparison to these results, prediction performances of models used extensively in the area of Food Microbiology, such as MLP and PLS, revealed their deficiencies, while ANFIS and SVM models revealed their robustness in providing acceptable prediction performances for either aerobic or MAP packaging conditions. Results evaluation indicated that the proposed modelling scheme could be considered as a valuable detection methodology in food microbiology.

Keywords

Fuzzy wavelet neural networks System identification Principal components analysis Meat spoilage Neural networks Clustering Classification 

Notes

Acknowledgements

The author would like to thank Dr. E.Z. Panagou from Agricultural University of Athens, Greece for providing the enose dataset, as well as the related microbiological analysis that correspond to the beef samples.

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Faculty of Science and TechnologyUniversity of WestminsterLondonUK

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