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,
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author = "David I. Ellis and David Broadhurst and
Douglas B. Kell and Jem J. Rowland and Royston Goodacre",
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title = "Rapid and Quantitative Detection of the Microbial
Spoilage of Meat by Fourier Transform Infrared
Spectroscopy and Machine Learning",
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journal = "Applied and Environmental Microbiology",
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year = "2002",
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volume = "68",
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number = "6",
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pages = "2822--2828",
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month = jun,
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keywords = "genetic algorithms, genetic programming",
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ISSN = "0099-2240",
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URL = "http://dbkgroup.org/Papers/app_%20env_microbiol_68_(2822).pdf",
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DOI = "doi:10.1128/AEM.68.6.2822-2828.2002",
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size = "7 pages",
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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.",
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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
Citations