Organised by Food Risk Management Systems Working Group
As we move to more digitised systems, these systems will be generating lots of data and we do not have time or the capability to deal with such large data sets. AI offers a way to manage all the data whether it be sensors on factory equipment, or CCTV in shops or abattoirs, or horizon scanning news items for unusual patterns of behaviour.
What you will learn about:
What is AI?
What are the current and potential future applications for AI?
Examples from various industry sectors and applications – not just food.
Novel data collection methods for food science that leverage AI e.g. the use of smart speakers, such as Amazon's Alexa devices, to collect data hands-free at the moment of product consumption.
Food industry in general with opportunities for Vulnerability Assessment, Engineering maintenance management to assessing consumer behaviour
Chair: Andy Kerridge, FIFST, Chair of IFST Midlands Branch
Andy has more than 25 years of experience in food technology/quality & safety management; working in Europe, Middle East & Africa. His career started in the meat industry and continued infoodservice. He has been involved in the development of issues 4-7 of the BRC Global Food Standard. Since 2012, he now helps companies meet demands of quality, safety & consistency. Andy is also Chair of IFST Midlands Branch.
Speaker: Jeremy Frey, Head Computational Systems Chemistry, University of Southampton
Jeremy Frey obtained his DPhil on experimental and theoretical aspects of van der Waals complexes, in the PCL, Oxford, followed by a NATO/SERC fellowship at the Lawrence Berkeley Laboratory with Prof. Yuan T. Lee. In 1984 he took up a lectureship at the University of Southampton, where he is now Professor of Physical Chemistry and head of the Computational Systems Chemistry Group. His experimental research probes molecular organization in environments from single molecules to liquid interfaces using laser spectroscopy from the IR to soft X-rays. In parallel he investigates how e-Science infrastructure helps to make a smart and intelligent laboratory. This blends into his computational and theoretical side he has a focus on chemical informatics and the application of novel mathematical approaches to chemical modelling. Jeremy is an enthusiastic supporter of interdisciplinary research, combining theory, computation and experiment within chemistry, and though the e-Science programme growing this to the wider computational and computing community together with industrial research. In his role in the Digital Economy programme through the IT as a Utility Network challenge area, and now the Internet of Food Things (www.foodchain.ac.uk), he addressed the full breadth of interdisciplinary research connecting social, physical, and life sciences in a trans-sectorial context bridging academic, commercial, and governmental areas. He is strongly committed to the need for collaborative, inter- and multi-disciplinary research and is skilled in facilitating communication between diverse disciplines. He is the PI of the new EPSRC Network+ on Artificial Intelligence and Automated Scientific Discovery encouraging the collaborations at the cutting edge of AI and Chemical Sciences. Jeremy is involved with the national and international chemical societies though the Royal Society of Chemistry (RSC) and the International Union of Pure and applied Chemistry (IUPAC) advocating engagement with the digital world.
Abstract: What is AI and why is it relevant?
What is Artificial Intelligence (AI) and the currently very topical sub-domain of Machine Learning (ML? How do they differ from other types of computer modelling, statistics, data science and big data, digital twins, etc.? As we dig deeper into the subject jargon like Deep Learning, Neural Networks, Transfer learning, generative adversarial networks, and transformers pop up. I will give a brief overview of the area and suggest applications of ML to the food sector from research to deliver. What new things can they bring to the food sector, what problems can they solve, and what problems (moral and ethical) do they create (or at least do not remove). For example, the problems of inherent bias prevalent in many applications and is driving the need for transparency and explainability. What about traditional AI will this help ML to achieve small learning and is the future really Augmented Intelligence?
Speaker: Dr Nicholas Watson, Associate Professor, Faculty of Engineering, University of Nottingham
Nik is an Associate Professor of Chemical Engineering. He has a PhD in engineering and spent four years as a researcher in the School of Food Science and Nutrition at the University of Leeds before joining Nottingham in 2014.
Nik’s research is focussed on digital manufacturing within the food and drink sector and his team develops intelligent sensor technologies to tackle some of the biggest challenges around sustainability, food safety, hygiene and productivity. A focus of Nik’s research is developing sensor and data analysis methods that work effectively in challenging food production environments and can be integrated with other key industrial digital technologies such as AI, Robotics and the Industrial Internet of Things. Nik has led projects investigating how sensors and data analytics can be used to reduce the cost and environmental impact of industrial cleaning processes and unit operations such as fermentation and mixing. Nik is currently a member of the EPSRC Early Career Forum in Manufacturing Research, on the Food Standards Agency register of experts and a Co-Investigator on the EPSRC digital manufacturing network: Connected Everything 2.
Abstract: Will Sensors and AI Revolutionise Food and Drink Manufacturing?
Industrial Digital Technologies (IDTs) such as robotics, Artificial Intelligence and the industrial internet of things are transforming manufacturing worldwide but what do these mean to the food and drink manufacturing sector and specifically food risk and safety? This presentation will focus on how low cost optical and ultrasonic sensor measurements and machine learning (a form of AI) can be used within food and drink manufacturing environments and focusses on three industrially relevant case studies:
• Clean-in-Place monitoring
• Allergen detection
• Poultry Inspection
Each case study will present the benefits of combining sensor measurements with machine learning in addition to discussing some of the current challenges that remain and methods required to overcome them.
Live Q&A: Andy Kerridge, Nik Watson and Jeremy Frey