A comparison of Raman and FT-IR spectroscopy for the prediction of meat spoilage

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@Article{Argyri2012,
  author =       "Anthoula A. Argyri and Roger M. Jarvis and 
                 David Wedge and Yun Xu and Efstathios Z. Panagou and 
                 Royston Goodacre and George-John E. Nychas",
  title =        "A comparison of Raman and FT-IR spectroscopy for the
                 prediction of meat spoilage",
  journal =      "Food Control",
  volume =       "29",
  number =       "2",
  pages =        "461--470",
  year =         "2013",
  note =         "Predictive Modelling of Food Quality and Safety",
  ISSN =         "0956-7135",
  DOI =          "doi:10.1016/j.foodcont.2012.05.040",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0956713512002745",
  keywords =     "genetic algorithms, genetic programming, Meat
                 spoilage, Raman spectroscopy, FT-IR, Multivariate
                 analysis, Evolutionary computing",
  abstract =     "In this study, time series spectroscopic,
                 microbiological and sensory analysis data were obtained
                 from minced beef samples stored under different
                 packaging conditions (aerobic and modified atmosphere
                 packaging) at 5 C. These data were analysed using
                 machine learning and evolutionary computing methods,
                 including partial least square regression (PLS-R),
                 genetic programming (GP), genetic algorithm (GA),
                 artificial neural networks (ANNs) and support vector
                 machines regression (SVR) including different kernel
                 functions [i.e. linear (SVRL), polynomial (SVRP),
                 radial basis (RBF) (SVRR) and sigmoid functions
                 (SVRS)]. Models predictive of the microbiological load
                 and sensory assessment were calculated using these
                 methods and the relative performance compared. In
                 general, it was observed that for both FT-IR and Raman
                 calibration models, better predictions were obtained
                 for TVC, LAB and Enterobacteriaceae, whilst the FT-IR
                 models performed in general slightly better in
                 predicting the microbial counts compared to the Raman
                 models. Additionally, regarding the predictions of the
                 microbial counts the multivariate methods (SVM, PLS)
                 that had similar performances gave better predictions
                 compared to the evolutionary ones (GA-GP, GA-ANN, GP).
                 On the other hand, the GA-GP model performed better
                 from the others in predicting the sensory scores using
                 the FT-IR data, whilst the GA-ANN model performed
                 better in predicting the sensory scores using the Raman
                 data. The results of this study demonstrate for the
                 first time that Raman spectroscopy as well as FT-IR
                 spectroscopy can be used reliably and accurately for
                 the rapid assessment of meat spoilage.",
}

Genetic Programming entries for Anthoula A Argyri Roger M Jarvis David C Wedge Yun Xu Efstathios Z Panagou Royston Goodacre George-John E Nychas

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