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Issue 7, 2016
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Meat, the metabolites: an integrated metabolite profiling and lipidomics approach for the detection of the adulteration of beef with pork

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Abstract

Adulteration of high quality food products with sub-standard and cheaper grades is a world-wide problem taxing the global economy. Currently, many traditional tests suffer from poor specificity, highly complex outputs and a lack of high-throughput processing. Metabolomics has been successfully used as an accurate discriminatory technique in a number of applications including microbiology, cancer research and environmental studies and certain types of food fraud. In this study, we have developed metabolomics as a technique to assess the adulteration of meat as an improvement on current methods. Different grades of beef mince and pork mince, purchased from a national retail outlet were combined in a number of percentage ratios and analysed using GC-MS and UHPLC-MS. These techniques were chosen because GC-MS enables investigations of metabolites involved in primary metabolism whilst UHPLC-MS using reversed phase chromatography provides information on lipophilic species. With the application of chemometrics and statistical analyses, a panel of differential metabolites were found for identification of each of the two meat types. Additionally, correlation was observed between metabolite content and percentage of fat declared on meat products’ labelling.

Graphical abstract: Meat, the metabolites: an integrated metabolite profiling and lipidomics approach for the detection of the adulteration of beef with pork

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Publication details

The article was received on 15 Jan 2016, accepted on 16 Feb 2016 and first published on 16 Feb 2016


Article type: Paper
DOI: 10.1039/C6AN00108D
Citation: Analyst, 2016,141, 2155-2164
  • Open access: Creative Commons BY license
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    Meat, the metabolites: an integrated metabolite profiling and lipidomics approach for the detection of the adulteration of beef with pork

    D. K. Trivedi, K. A. Hollywood, N. J. W. Rattray, H. Ward, D. K. Trivedi, J. Greenwood, D. I. Ellis and R. Goodacre, Analyst, 2016, 141, 2155
    DOI: 10.1039/C6AN00108D

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