Chemometric method development for metabolomic analyses for food security and authentication

This project aims to develop novel methods for the analysis of large datasets from metabolomics and transcriptomics in order to further understanding that will contribute to worldwide food security, reduce soya importation and benefit the EU economy and the environment. In view of climate change, understanding the mechanisms that allow some plants to withstand drought and disease is vital. Time series data will be analysed to see how these factors affect the growth of leguminous plants, such as peas. The aim is to analyse data from plants subjected to both drought and Fusarium-infection using sophisticated statistical and pattern-recognition techniques. In particular, to combine data obtained from the plant DNA with data from metabolomics to identify the genes making a plant resistant to drought and/or disease with data from experiments to identify the chemicals within the plant that provide information on the metabolic pathways involved in resistance to fungal infection and drought. Mass-spectrometry and nuclear magnetic resonance (NMR)-based techniques will be used in non-targeted metabolomic analysis combined with data from QTL (Quantitative Trait Locus) mapping experiments. The fusion of these 'omics datasets is not trivial novel multivariate methods based on correlation or concatenation as well as pathway-based methods will be developed. The combination of data from different technologies will require the development of new techniques and the results could lead to the rapid identification of more resilient crop varieties.
Another aim of this project is to improve understanding of the nutritional requirements of black soldier fly (BSF) larvae for optimal conversion of food waste streams to insect biomass. Insects, especially flies, have the potential to provide a protein source for animal feed as an alternative to increasingly expensive imported soya and fishmeal, thereby improving the carbon footprint and reducing overfishing. Fly larvae are a natural component of the diet of fish, chicken and pigs and can be grown on a range of organic wastes, reducing the volume of that waste by up to 60%, providing an additional benefit to waste management and the environment. To achieve this, the substrates obtained from available food waste products via degradation by microorganisms (anaerobic digestion), will be profiled using mass spectrometry and NMR and correlated with larval protein yield to allow the minimum required content of nutrients such as nitrogen, protein and fats to be determined. Analysis of the nutritional quality of various bio-degraded food waste streams will also be performed and methods to model the combination of various waste streams to optimize the nutritional quality of feed substrate developed. In the same way that commodity prices are used to determine the most economical composition of animal feed that satisfies regulations, prototype software will be developed to integrate information on the content of essential nutrients from available waste streams to optimise substrate production and hence insect protein yield.
Keywords
Chemometrics, statistics, data analysis, pattern recognition, mathematical modelling