Rapid and quantitative detection of the microbial spoilage of beef by Fourier transform infrared spectroscopy and machine learning

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@Article{Ellis:2004:ACA,
  author =       "David I. Ellis and David Broadhurst and 
                 Royston Goodacre",
  title =        "Rapid and quantitative detection of the microbial
                 spoilage of beef by Fourier transform infrared
                 spectroscopy and machine learning",
  journal =      "Analytica Chimica Acta",
  year =         "2004",
  volume =       "514",
  pages =        "193--201",
  number =       "2",
  abstract =     "Beef is a commercially important and widely consumed
                 muscle food and central to the protein intake of many
                 societies. In the food industry no technology exists
                 for the rapid and accurate detection of
                 microbiologically spoiled or contaminated beef. Fourier
                 transform infrared (FT-IR) spectroscopy is a rapid,
                 reagentless and non-destructive analytical technique
                 whose continued development is resulting in manifold
                 applications across a wide range of biosciences. FT-IR
                 was exploited to measure biochemical changes within the
                 fresh beef substrate, enhancing and accelerating the
                 detection of microbial spoilage. Separately packaged
                 fresh beef rump steaks were purchased from a national
                 retailer, comminuted for 15 s and left to spoil at
                 ambient room temperature for 24 h. Every hour, FT-IR
                 measurements were collected directly from the sample
                 surface using attenuated total reflectance, in parallel
                 the total viable counts of bacteria were obtained by
                 classical microbiological plating methods. Quantitative
                 interpretation of FT-IR spectra was undertaken using
                 partial least squares regression and allowed for
                 accurate estimates of bacterial loads to be calculated
                 directly from the meat surface in 60 s. Machine
                 learning methods in the form of genetic algorithms and
                 genetic programming were used to elucidate the
                 wavenumbers of interest related to the spoilage
                 process. The results obtained demonstrated that using
                 FT-IR and machine learning it was possible to detect
                 bacterial spoilage rapidly in beef and that the most
                 significant functional groups selected could be
                 directly correlated to the spoilage process which arose
                 from proteolysis, resulting in changes in the levels of
                 amides and amines.",
  owner =        "wlangdon",
  URL =          "http://dbkgroup.org/dave_files/ACAbeef04.pdf",
  URL =          "http://www.sciencedirect.com/science/article/B6TF4-4CDJJ78-5/2/63df147cb89407ac7ac8bf9d093580f7",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1016/j.aca.2004.03.060",
}

Genetic Programming entries for David I Ellis David I Broadhurst Royston Goodacre

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