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Title: The use of metabonomic profiling approaches for the investigation of complex food fraud
Author: Sidwick, Kate Louise
ISNI:       0000 0004 7659 3973
Awarding Body: Keele University
Current Institution: Keele University
Date of Award: 2019
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Food fraud is a challenge in today's expanding global food industry. Recently weaknesses in current testing methods for meat authentication have been exposed. Labels are assumed to accurately describe the contents of meat products, however these can be easily manipulated. Consumers must have confidence in food products for various reasons, including allergies and religious beliefs. Techniques have been created to target obvious types of fraud, however the more subtle types remain difficult to combat. This work aimed to understand the chemical composition of meat products in order to develop methods that can tackle complex frauds. The development of a data processing and statistical workflow sufficient for vast untargeted metabonomic datasets was also essential for this research. Liquid chromatography quadrupole time-of-flight mass spectrometry, with robust quality control procedures and multivariate statistics, were used to measure changes in the metabolic profile of meat samples. Specifically, the differentiation between normally slaughtered and dead on arrival chicken was achieved, and sphingosine was identified as a key marker in the muscle tissue. An investigation into the duration of frozen storage and freeze-thaw cycling of meat found the fatty acid degradation pathways were most affected. The adulteration of minced beef products with other meat species yielded the tentative identifications of several markers that could be used to detect adulterated beef products regardless of whether the meat is raw or cooked. Finally, the metabolic changes occuring during the spoilage of chicken were observed, and showed that amino acid and fatty acid concentration could be used to determine the shelf-life of meat products. The methodologies that have been presented in this work have shown potential to be implemented and developed as robust detection methods to combat subtle food frauds in the future.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: QD Chemistry