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


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.


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



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.


  1. Abiyev, R.H., Kaynak, O. (2008a). Identification and control of dynamic plants using fuzzy wavelet neural networks, Proc. of the IEEE International Symposium on Intelligent Control, 1295–1301.Google Scholar
  2. Abiyev, R. H., & Kaynak, O. (2008b). Fuzzy wavelet neural networks for identification and control of dynamic plants - a novel structure and a comparative study. IEEE Transactions on Industrial Electronics, 55(8), 3133–3140.CrossRefGoogle Scholar
  3. Al-Anazi, A., Gates, I.D. (2010). Support-vector regression for permeability prediction in a heterogeneous reservoirs: SPE 126339, SPE Reservoir Evaluation & Engineering 485–495.Google Scholar
  4. Alshejari, A., Kodogiannis, V.S. (2016). An Intelligent Decision Support System for the Detection of Meat Spoilage using Multispectral Images, Neural Computing and Applications, (In Press).Google Scholar
  5. Amamcharla, J. K., Panigrahi, S., Logue, C. M., Marchello, M., & Sherwood, J. S. (2010). Fourier transform infrared spectroscopy (FTIR) as a tool for discriminating salmonella typhimurium contaminated beef. Sensing and Instrumentation for Food Quality and Safety, 4(1), 1–12.CrossRefGoogle Scholar
  6. Amina, M., Panagou, E. Z., Kodogiannis, V. S., & Nychas, G.-J. E. (2010). Wavelet neural networks for modelling high pressure inactivation kinetics of Listeria monocytogenes in UHT whole milk. Chemometrics and Intelligent Laboratory Systems, 103(2), 170–183.CrossRefGoogle Scholar
  7. Amina, M., Kodogiannis, V. S., Petrounias, I., Lygouras, J. N., & Nychas, G.-J. E. (2012). Identification of the Listeria monocytogenes survival curves in UHT whole milk utilising local linear wavelet neural networks. Expert Systems and Applications, 39(1), 1435–1450.CrossRefGoogle Scholar
  8. Argyri, A. A., Panagou, E. Z., Tarantilis, P. A., Polysiou, M., & Nychas, G.-J. E. (2010). Rapid qualitative and quantitative detection of beef fillets spoilage based on Fourier transform infrared spectroscopy data and artificial neural networks. Sensors and Actuators B, 145, 146–154.CrossRefGoogle Scholar
  9. Baietto, M., Wilson, A. D., Bassi, D., & Ferrini, F. (2010). Evaluation of three electronic noses for detecting incipient wood decay. Sensors, 10, 1062–1092.CrossRefGoogle Scholar
  10. Balasubramanian, S., Panigrahi, S., Logue, C. M., Doetkott, C., Marchello, M., & Sherwood, J. S. (2008). Independent component analysis-processed electronic nose data for predicting salmonella typhimurium populations in contaminated beef. Food Control, 19(3), 236–246.CrossRefGoogle Scholar
  11. Balasubramanian, S., Amamcharla, J., Shin, J.-E. (2016). Possible Application of Electronic Nose Systems for Meat Safety: An Overview, Electronic Noses and Tongues in Food Science, 59–71.Google Scholar
  12. Berna, A.Z., Anderson, A.R., Trowell, S.C. (2009). Bio-benchmarking of electronic nose sensors, PLoS ONE, 4(7).Google Scholar
  13. Boothe, D. D. H., & Arnold, J. W. (2002). Electronic nose analysis of volatile compounds from poultry meat samples, fresh and after refrigerated storage. Journal of the Science of Food and Agriculture, 82(3), 315–322.CrossRefGoogle Scholar
  14. Capelli, L., Sironi, S., & Del Rosso, R. (2014). Electronic noses for environmental monitoring applications. Sensors, 14(11), 19979–20007.CrossRefGoogle Scholar
  15. Casaburi, A., Piombino, P., Nychas, G. J., Villani, F., & Ercolini, D. (2015). Bacterial populations and the volatilome associated to meat spoilage. Food Microbiology, 45(Pt A), 83–102.CrossRefGoogle Scholar
  16. Christiansen, A.N., Carstensen, J.M., Papadopoulou, O., Chorianopoulos, N., Panagou, E.Z., & Nychas, G-J.E (2011). Multi spectral imaging analysis for meat spoilage discrimination, 7th International Conference on Predictive Modelling of Food Quality and Safety, Dublin, Ireland.Google Scholar
  17. Di Natale, C., Macagnano, A., & D’Amico, A. (1998). Electronic nose and sensorial analysis: comparison of performances in selected cases. Sensors & Actuators B, 50, 246–252.CrossRefGoogle Scholar
  18. Di Natale, C., Macagnano, A., Martinelli, E., Paolesse, R., & Proietti, E. (2001). A D’Amico, the evaluation of quality of post-harvest oranges and apples by means of an electronic nose. Sensors & Actuators B Chem, 78, 26–31.CrossRefGoogle Scholar
  19. Ehret, B., Safenreiter, K., Lorenz, F., & Biermann, J. (2011). A new feature extraction method for odour classification. Sens. Actuators B Chem., 158, 75–88.CrossRefGoogle Scholar
  20. Ellis, D. I., & Goodacre, R. (2001). Rapid and quantitative detection of the microbial spoilage of muscle foods: current status and future trends. Trends in Food Science & Technology, 12, 414–424.CrossRefGoogle Scholar
  21. Fend, R., Kolk, A. H. J., Bessant, C., Buijtels, P., Klatser, P. R., & Woodman, A. C. (2006). Prospects for clinical application of electronic-nose technology to early detection of mycobacterium tuberculosis in culture and sputum. Journal of Clinical Microbiology, 44(6), 2039–2045.CrossRefGoogle Scholar
  22. Ghasemi-Varnamkhasti, M., Mohtasebi, S. S., Siadat, M., & Balasubramanian, S. (2009). Meat quality assessment by electronic nose. Sensors, 9(8), 6058–6083.CrossRefGoogle Scholar
  23. Gill, C. O., & Jeremiah, L. E. (1991). The storage life of non-muscle offals packaged under vacuum or carbon dioxide. Food Microbiology, 8, 339–353.CrossRefGoogle Scholar
  24. Hubert, M., Ousseeuw, P., & Branden, K. (2005). ROBPCA: a new approach to robust principal component analysis. Technometrics, 47(1), 64–79.CrossRefGoogle Scholar
  25. Jang, J.S.R. Sun, C.T., Mizutani, E. (1997). Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence, Prentice-Hall.Google Scholar
  26. Khot, L. R., Panigrahi, S., Doetkott, C., & Chang, Y. (2012). Evaluation of technique to overcome small dataset problems during neural-network based contamination classification of packaged beef using integrated olfactory sensor system. LWT - Food Science and Technology, 45(2), 233–240.CrossRefGoogle Scholar
  27. Kodogiannis, V.S., & Petrounias, I. (2012). Modelling of survival curves in food microbiology using adaptive fuzzy inference neural networks, 2012 IEEE Int. Conf. on Computational Intelligence for Measurement Systems and Applications IEEE (CIMSA 2012), IEEE. pp. 35–40. doi: 10.1109/CIMSA.2012.6269596.
  28. Kodogiannis, V. S., & Alshejari, A. (2014). An adaptive neuro-fuzzy identification model for the detection of meat spoilage. Applied Soft Computing, 23, 483–497.CrossRefGoogle Scholar
  29. Kodogiannis, V.S., Amina, M., Petrounias, I. (2013). A clustering-based fuzzy-wavelet neural network model for short-term load forecasting, International Journal of Neural Systems, 23(5).Google Scholar
  30. Kodogiannis, V., Pachidis, T., & Kontogianni, E. (2014). An intelligent based decision support system for the detection of meat spoilage. Engineering Applications of Artificial Intelligence, 34, 23–36.CrossRefGoogle Scholar
  31. Lee, C. C. (1990). Fuzzy logic in control systems: fuzzy logic controller—part I & II. IEEE Trans. Syst. Man Cybern.SMC-20, 2, 404–435.CrossRefGoogle Scholar
  32. Lee, D. S., Lee, M. W., Woo, S. H., Kim, Y.-J., & Park, J. M. (2006). Nonlinear dynamic partial least squares modeling of a full-scale biological wastewater treatment plant. Process Biochemistry, 41(9), 2050–2057.CrossRefGoogle Scholar
  33. Li, S.,Wang, P., Goel, L. (2016). A Novel Wavelet-Based Ensemble Method for Short-Term Load Forecasting with Hybrid Neural Networks and Feature Selection, IEEE Transactions on power systems, 31(3) .Google Scholar
  34. Meisel, S., Stöckel, S., Rösch, P., & Popp, J. (2014). Identification of meat-associated pathogens via Raman microspectroscopy. Food Microbiology, 38, 36–43.CrossRefGoogle Scholar
  35. Nelles, O. (2001). Nonlinear system identification: from classical approaches to Neura lNetworks and fuzzy models. Berlin: Springer.CrossRefGoogle Scholar
  36. Nurjuliana, M., Che Man, Y. B., Mat Hashim, D., & Mohamed, A. K. S. (2011). Rapid identification of pork for halal authentication using the electronic nose and gas chromatography mass spectrometer with headspace analyzer. Meat Science, 88(4), 638–644.CrossRefGoogle Scholar
  37. O’Sullivan, M.G., Kerry, J.P. (2009). Sensory evaluation of fresh meat, in Improving the sensory and nutritional quality of fresh meat, Woodhead Publishing Limited.Google Scholar
  38. Panagou, E. Z., & Kodogiannis, V. (2009). Application of neural networks as a non-linear modelling technique in food mycology. Expert Systems with Applications, 36, 121–131.CrossRefGoogle Scholar
  39. Panagou, E. Z., Kodogiannis, V., Nychas, G.J-E. (2007). Modelling fungal growth using radial basis function neural networks: The case of the ascomycetous fungus Monascus ruber van Tieghem. International Journal of Food Microbiology, 117, 276--286.Google Scholar
  40. Papadopoulou, O., Panagou, E. Z., Mohareb, F., & Nychas, G.-J. (2013). Sensory and microbiological quality assessment of beef fillets, using a portable electronic nose in tandem with support vector machine analysis. Food Research International, 50, 241–249.CrossRefGoogle Scholar
  41. Quan, T., Liu, X., & Liu, Q. (2010). Weighted least squares support vector machine local region method for non linear time series prediction. Applied Soft Computing, 10(2), 562–566.CrossRefGoogle Scholar
  42. Ross, E. W., Taub, I. A., Doona, C. J., Feeherry, F. E., & Kustin, K. (2005). The mathematical properties of the quasi-chemical model for microorganism growth-death kinetics in foods. International Journal of Food Microbiology, 99, 157–171.CrossRefGoogle Scholar
  43. Rutkowska, D. (2002). Neuro-Fuzzy Architectures and Hybrid Learning, Springer.Google Scholar
  44. Skandamis, P., & Nychas, G. J. (2002). Preservation of fresh meat with active and modified atmosphere packaging conditions, Int. J. Food Microbiology, 79, 35–45.CrossRefGoogle Scholar
  45. Song, S., Yuan, L., Zhang, X., & Hayat, K. (2013). Rapid measuring and modelling flavour quality changes of oxidised chicken fat by electronic nose profiles through the partial least squares regression analysis. Food Chemistry, 141(4), 4278–4288.CrossRefGoogle Scholar
  46. Tao, F., & Peng, Y. (2014). A method for non-destructive prediction of pork meat quality and safety attributes by hyperspectral imaging technique. Journal of Food Engineering, 126, 98–106.CrossRefGoogle Scholar
  47. Tian, X., Wang, J., & Cui, S. (2013). Analysis of pork adulteration in minced mutton using electronic nose of metal oxide sensors. Journal of Food Engineering, 119(4), 744–749.CrossRefGoogle Scholar
  48. Valipour, M. (2016). Optimization of neural networks for precipitation analysis in a humid region to detect drought and wet year alarms. Meteorological Applications, 23(1), 91–100.CrossRefGoogle Scholar
  49. Valipour, M., Banihabib, M. E., & Behbahani, S. M. R. (2013). Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir. Journal of Hydrology, 476, 433–441.CrossRefGoogle Scholar
  50. Vapnik, V. (1998). Statistical learning theory. New York: Wiley.Google Scholar
  51. Wang, D., Wang, X., Liu, T., & Liu, Y. (2012). Prediction of total viable counts on chilled pork using an electronic nose combined with support vector machine. Meat Science, 90, 373–377.CrossRefGoogle Scholar

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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