Exploration of Novel Technologies to Provide Rapid and Cost-Effective Methods for Counteracting Food Fraud
- Project start date: 1 October 2014
- Project status: Completed
- Project type: Food safety
- Discipline: Food chain
- Principal researcher/s: Dr Simon Haughey
- Collaborator/s: Prof Tom Buckley, IEC, Dr Olivier Chevallier, QUB Mass Spectrometry Centre, Dr Tassos Koidis, Queen’s University of Belfast
Research objective
Food fraud is a serious and global whole food chain phenomenon with negative impacts on consumer confidence and potentially their health and well-being. There is now an urgency to develop cost-effective, rapid and reliable analytical methodologies that can be used for screening suspect foods for their authenticity. This research was carried out at the Institute for Global Food Security at Queen's University Belfast, in collaboration with the Irish Equine Centre in Co. Kildare, and investigated a number of analytical techniques in food authenticity testing.
FT-IR had advantages in the classification of vegetable oils, while NMR also had advantages due to its ease of use and quick analysis time. The LAMP assay was applied to cheese, fish and meat speciation and proved useful for single species identification. Using LAMP, some goat’s cheese samples were found to contain significant levels of sheep DNA: further surveying of cheese samples would be necessary to determine if this finding is indicative of fraudulent activity. REIMS was used for the fast and accurate speciation of meat and fish, including the possible determination of species and the method of catch, and could be applied to detect fish fraud within the global seafood supply chain. Application of the REIMS technique for the rapid lipidomic profiling of food-grade meat products was successfully performed for the first time.
Outputs
Research report
Other outputs
Peer reviewed article
Connor Black, Olivier P. Chevallier, Simon A. Haughey, Julia Balog, Sara Stead, Steven D. Pringle, Maria V. Riina, Francesca Martucci, Pier L. Acutis, Mike Morris, Dimitrios S. Nikolopoulos, Zoltan Takats and Christopher T. Elliott. A real time metabolomic profiling approach to detecting fish fraud using rapid evaporative ionisation mass spectrometry. Metabolomics (2017) 13:153, pp2-13.
