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.
Original language | English |
---|---|
Pages (from-to) | 2155-2164 |
Number of pages | 10 |
Journal | Analyst |
Volume | 141 |
Issue number | 7 |
Early online date | 16 Feb 2016 |
DOIs | |
Publication status | Published - 7 Apr 2016 |
Funding
We thank colleagues at the Centre for Advanced Discovery and Experimental Therapeutics (CADET) at the University of Manchester, part of Manchester Academic Health Science Centre and facilitated by the Manchester Biomedical Research Centre and the NIHR Greater Manchester Comprehensive Local Research Network, for access to their TissueLyser. RG thanks BBSRC for support for metabolomics (Grant number BB/C519038/1). RG and DIE also thank the ESRC and FSA for funding (Food fraud: a supply network integrated systems analysis (Grant number ES/M003183/1)).
Keywords
- food product adulteration
- metabolomics
- food fraud
- beef
- pork