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

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dc.contributor.author Broadhurst, David I.
dc.contributor.author Ellis, David I.
dc.contributor.author Goodacre, Royston
dc.date.accessioned 2009-09-09T08:42:50Z
dc.date.available 2009-09-09T08:42:50Z
dc.date.issued 2004-07-01
dc.identifier.citation Broadhurst , D I , Ellis , D I & Goodacre , R 2004 , ' Rapid and quantitative detection of the microbial spoilage of beef by Fourier transform infrared spectroscopy and machine learning ' Analytica Chimica Acta , vol 514 , no. 2 , pp. 193-201 . en
dc.identifier.issn 0003-2670
dc.identifier.other PURE: 121359
dc.identifier.other dspace: 2160/3019
dc.identifier.uri http://hdl.handle.net/2160/3019
dc.description Ellis, D. I., Broadhurst, D., Goodacre, R. (2004). Rapid and quantitative detection of the microbial spoilage of beef by Fourier transform infrared spectroscopy and machine learning.   Analytica Chimica Acta, 514, (2), 193-201 Sponsorship: Agri-Food and Engineering and Biological Systems Committees of the UK BBSRC en
dc.description.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. en
dc.format.extent 9 en
dc.language.iso eng
dc.relation.ispartof Analytica Chimica Acta en
dc.title Rapid and quantitative detection of the microbial spoilage of beef by Fourier transform infrared spectroscopy and machine learning en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.identifier.doi http://dx.doi.org/10.1016/j.aca.2004.03.060
dc.contributor.institution Institute of Biological, Environmental and Rural Sciences en
dc.description.status Peer reviewed en


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