AI and Ensemble approaches to model transient lipid deterioration in particulate food systems

Lipids are a major constituent of foods which are worth around USD 50 Billion. Unsaturated lipid components [e.g. linoleic, arachidonic, eicosapentaenoic and docosahexaenoic acids] undergo oxidation which severely impacts on food quality and shelf-stability through flavour and taste deterioration and decreases in nutritive value. This project hypothesises that Artificial Intelligence and Machine Learning allow prediction of quality changes occurring in a product, or to formulate new food products which are stable. Such models result in a substantial reduction in food waste, time and resources.

The student will gain considerable training in experimental and mathematical modelling methods used in shelf-life and keeping quality assessment at the University of Reading. In addition, the student will be trained in the use of latest Machine Learning and Artificial Intelligence methods and apply these to food systems - which is still an area in its infancy.