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

Meat remains a primary source of protein for much of the public, but as the public becomes increasingly aware of the environmental impacts around meat production, consumers are looking for meat that is not only of high quality but also has lower environmental impacts and higher ethical standards. Therefore, automated quality control is of high importance for meeting consumers’ requirements while minimising the environmental impact and maximising production.

Therefore, the project Automated Robotic Rapid Evaporative Ionization Mass Spectrometry Meat Sampling (AR2MS), will develop and apply Machine Learning methods for computer vision, robot control and analysis of rapid mass spectrometry data to instantly deliver assessments on the meat quality, geographical origin, and breed.  This project will deliver, in combination with a major meat producer and a provider of scientific testing solutions, a commercially viable system suitable for deployment in the business of that major meat producer. Therefore, the key research areas of the project are on Machine learning methods for robot perception and control, AI data analysis and decision-making from rapid mass spectrometry data.

During the project, the successful candidate will have close collaboration with a multidisciplinary team of experts in AI, robotics, mass spectrometry and food manufacturing. Moreover, they will collaborate with the R&D team of Cranswick, a major meat producer, and they will be supported by the scientific testing provider in the development of the computational modelling needed to deliver a proof of concept for the quality control system. The successful candidate is expected to have experience in machine learning with additional experience in robotics and/or rapid mass spectrometry considered as a plus. The candidate will gain skills in computer vision, robotics, digital modelling, data management, systems integration and to a lesser extent, exposure to analytical systems used in food production systems.