The many challenges relating to environmental sustainability and climate change are major global concerns. Finding sustainable and efficient ways to provide sufficient, nutritious food, especially in relation to protein sources in conjunction with reducing carbon dioxide emissions produced by livestock, is amongst the top priorities. Emerging alternative sources of proteins from plants, insects, algae, microbes, fermentation, and cell culture have shown varying degrees of potential as complementary and/or replacement approaches to animal protein industry with large profit margins.
Health risks associated with consumption of alternative proteins have not been well studied and are therefore a major knowledge gap for the food industry and regulatory authorities. Fraud could be a major contributor to these risks. Protein scandals caused by horsemeat in Europe and melamine in China are both infamous and caused massive public concern. The potential for the next scandal to be caused by an alternative protein is high.
The project is closely aligned to recommendations made from the Food Standards Agency and UK Research and Innovation strategy. Thus, cutting edge science and technology is being applied to deal with identified real world problems through multidisciplinary approach and mutual knowledge sharing across both academia and industry.
This project will exploit the breadth of cutting-edge analytical technology platforms and associated expertise in the ASSET Technology Centre under the UK National Measurements Laboratory programme. It will also exploit the knowledge and experience of the UK food industry through the Food Industry Intelligence Network (Fiin) and increase the current level of understanding of fraud risks in the global alternative protein supply chains. Following engagement with key stakeholders a priority ranking of adulteration risks will be in place and multidisciplinary solutions will be delivered to allow fit for purpose monitoring programmes. This will be followed by further methodology development with three targets: (a) detection of DNA (b) nitrogen rich chemical adulterant detection (c) identification of adulterant proteins. For DNA, CRISPR-Cas systems with SERS and nanopore will be used to develop rapid and portable on-site solutions. For nitrogen rich chemical adulterants and protein adulterants, laboratory-based LC-HRMS will be employed to build a range of databases (for compounds and peptides). Selected machine learning algorithms will be employed as part of the data processing to further enhance method performance. Eventually a multidisciplinary toolbox for different end users against adulterants will be delivered.
Training opportunities:
The successful applicant will have the opportunity to work with researchers at Queen’s University Belfast, LGC, and industrial partners from Fiin, and Agilent etc. The student will be hugely benefited through the globalized network (not only UK but also in EU countries and Asian-Pacific areas) and gain knowledge towards alternative proteins.
Students will be receiving multidisciplinary training including social studies (surveys) and a wide range of analytical tools: LC-MS (Peptides and chemical contaminants), SERS, nanopore, together with data processing with chemometric software and machine learning algorithms.
Opportunities for public engagement and communication skills will be further developed through the frequent interactivities and workshops with industrial partners and regulators.
The student will also be able to attend national and international conferences and exchanging/internship experiences in those UK enterprises and EU/Chinese top food science institutes.
Skills/experience required: Students from biological, analytical chemistry, food science backgrounds and certain other degrees are welcomed to apply to this PhD project.
Preferred skills are lab experiences and knowledge in one of the following areas:
Surface enhanced Raman Spectroscopy
CRISPR-Cas and molecular cloning
LC-MS (For small molecules or proteomic workflows)
Advanced Skills (Bonus & priority):
Nanopore assembling
Machine learning algorisms (Using R or MATLAB)
Protein de novo sequencing
This project will be supervised by Dr Di Wu and Professor Chris Elliott.
Start Date: 1 October 2023
Duration: 3 years full-time
How to apply: Applicants must be submitted via: https://dap.qub.ac.uk/portal/user/u_login.php
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