Rapid and Quantitative Detection of the Microbial Spoilage of Meat by Fourier Transform Infrared Spectroscopy and Machine Learning

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@Article{ellis:2002:AEM,
  author =       "David I. Ellis and David Broadhurst and 
                 Douglas B. Kell and Jem J. Rowland and Royston Goodacre",
  title =        "Rapid and Quantitative Detection of the Microbial
                 Spoilage of Meat by Fourier Transform Infrared
                 Spectroscopy and Machine Learning",
  journal =      "Applied and Environmental Microbiology",
  year =         "2002",
  volume =       "68",
  number =       "6",
  pages =        "2822--2828",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "0099-2240",
  URL =          "http://dbkgroup.org/Papers/app_%20env_microbiol_68_(2822).pdf",
  DOI =          "doi:10.1128/AEM.68.6.2822-2828.2002",
  size =         "7 pages",
  abstract =     "Fourier transform infrared (FT-IR) spectroscopy is a
                 rapid, noninvasive technique with considerable
                 potential for application in the food and related
                 industries. We show here that this technique can be
                 used directly on the surface of food to produce
                 biochemically interpretable ?fingerprints.? Spoilage in
                 meat is the result of decomposition and the formation
                 of metabolites caused by the growth and enzymatic
                 activity of microorganisms. FT-IR was exploited to
                 measure biochemical changes within the meat substrate,
                 enhancing and accelerating the detection of microbial
                 spoilage. Chicken breasts were purchased from a
                 national retailer, comminuted for 10 s, and left to
                 spoil at room temperature for 24 h. Every hour, FT-IR
                 measurements were taken directly from the meat surface
                 using attenuated total reflectance, and the total
                 viable counts were obtained by classical plating
                 methods. Quantitative interpretation of FT-IR spectra
                 was possible using partial least-squares regression and
                 allowed accurate estimates of bacterial loads to be
                 calculated directly from the meat surface in 60 s.
                 Genetic programming was used to derive rules showing
                 that at levels of 10000000 bacteria per gram 1 the main
                 biochemical indicator of spoilage was the onset of
                 proteolysis. Thus, using FT-IR we were able to acquire
                 a metabolic snapshot and quantify, noninvasively, the
                 microbial loads of food samples accurately and rapidly
                 in 60 s, directly from the sample surface. We believe
                 this approach will aid in the Hazard Analysis Critical
                 Control Point process for the assessment of the
                 microbiological safety of food at the production,
                 processing, manufacturing, packaging, and storage
                 levels.",
  notes =        "American Society for Microbiology PMID: 12039738",
}

Genetic Programming entries for David I Ellis David I Broadhurst Douglas B Kell Jem J Rowland Royston Goodacre

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