AR2MS – Automated Robotic Rapid evaporative ionisation mass spectrometry Meat Sampling

SUSTAIN Student:

Albor Meha

Supervisory Team:

Dr Athanasios Polydoros (University of Lincoln), Dr Nick Birse (Queen's University Belfast), Prof Mark Swainson (University of Lincoln)

Meat continues to be a vital source of protein for many people. As awareness grows about the environmental impacts of meat production, consumers are increasingly seeking meat that is not only high quality but also produced with lower environmental impacts and higher ethical standards. Automated quality control is crucial for meeting these evolving consumer demands while minimising environmental impact and maximising production efficiency.

The Automated Robotic Rapid Evaporative Ionization Mass Spectrometry Meat Sampling (AR2MS) project is set to revolutionise this space. By developing and applying cutting edge machine learning methods for computer vision, robot control and rapid mass spectrometry data analysis, AR2MS will provide instant assessments of meat quality, geographical origin, and breed. This innovative project, in collaboration with a major meat producer and a provider of scientific testing solutions, aims to deliver a commercially viable system ready for deployment.

Key research areas include machine learning methods for robot perception and control, AI data analysis and decision-making from rapid mass spectrometry data. The successful candidate will work closely with a multidisciplinary team of experts in AI, robotics, mass spectrometry and food manufacturing. They will also collaborate with the Research and Development team at Cranswick PLC, a leading meat producer, and receive support from the scientific testing provider to develop the computational models needed for the quality control system.

We are excited to offer training for PhD students who may not yet have expertise in areas such as machine learning, robotics, or rapid mass spectrometry. This project provides an excellent opportunity for the successful candidate to gain valuable skills in computer vision, robotics, digital modelling, data management, systems integration, and exposure to analytical systems used in food production.

Join us in shaping the future of sustainable and ethical food production!