Abstract
Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. To address the rapid and non-destructive detection of meat spoilage, Fourier transform infrared (FTIR) spectroscopy with the aid of an intelligent decision system, was considered in this work. FTIR spectra were obtained from the surface of beef samples at various temperatures, while a microbiological analysis identified the population of total viable counts for each sample. An adaptive fuzzy logic system model that utilizes a prototype defuzzification scheme has been developed to classify beef samples in their respective quality class and to predict simultaneously their associated microbiological population directly from FTIR spectra. Results confirmed the superiority of the adopted methodology and indicated that FTIR spectral information in combination with an efficient choice of a modeling scheme could be considered as an alternative methodology for the accurate evaluation of meat spoilage.
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Kodogiannis, V.S., Petrounias, I., Lygouras, J. (2015). An Intelligent Based Decision Support System for the Detection of Meat Spoilage. In: Filev, D., et al. Intelligent Systems'2014. Advances in Intelligent Systems and Computing, vol 323. Springer, Cham. https://doi.org/10.1007/978-3-319-11310-4_26
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DOI: https://doi.org/10.1007/978-3-319-11310-4_26
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11309-8
Online ISBN: 978-3-319-11310-4
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