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Applied and Environmental Microbiology, June 2002, p. 2822-2828, Vol. 68, No. 6
0099-2240/02/$04.00+0     DOI: 10.1128/AEM.68.6.2822-2828.2002
Copyright © 2002, American Society for Microbiology. All Rights Reserved.

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

David I. Ellis,1 David Broadhurst,1 Douglas B. Kell,1 Jem J. Rowland,2 and Royston Goodacre1*

Institute of Biological Sciences,1 Department of Computer Sciences, University of Wales, Aberystwyth, Ceredigion SY23 3DD, Wales, United Kingdom2

Received 13 September 2001/ Accepted 14 March 2002

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 107 bacteria·g-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.

* Corresponding author. Mailing address: Institute of Biological Sciences, University of Wales, Aberystwyth, Ceredigion SY23 3DD, United Kingdom. Phone: 44 (0)1970 621947. Fax: 44 (0)1970 621947. E-mail: rrg{at}aber.ac.uk.

Applied and Environmental Microbiology, June 2002, p. 2822-2828, Vol. 68, No. 6
0099-2240/02/$04.00+0     DOI: 10.1128/AEM.68.6.2822-2828.2002
Copyright © 2002, American Society for Microbiology. All Rights Reserved.



This article has been cited by other articles:

  • Al-Qadiri, H. M., Lin, M., Al-Holy, M. A., Cavinato, A. G., Rasco, B. A. (2008). Monitoring Quality Loss of Pasteurized Skim Milk Using Visible and Short Wavelength Near-Infrared Spectroscopy and Multivariate Analysis. J DAIRY SCI 91: 950-958 [Abstract] [Full Text]  
  • Jarvis, R. M., Goodacre, R. (2005). Genetic algorithm optimization for pre-processing and variable selection of spectroscopic data. Bioinformatics 21: 860-868 [Abstract] [Full Text]  
  • Johnson, H. E., Broadhurst, D., Kell, D. B., Theodorou, M. K., Merry, R. J., Griffith, G. W. (2004). High-Throughput Metabolic Fingerprinting of Legume Silage Fermentations via Fourier Transform Infrared Spectroscopy and Chemometrics. Appl. Environ. Microbiol. 70: 1583-1592 [Abstract] [Full Text]  


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