Metabolomics is a broad scientific field with exciting applications in food and agriculture. It provides a snapshot of the metabolites present in a biological system at a specific point in time or in a specific genotype, and it allows the monitoring of metabolic changes occurring in response to environmental stimuli.
We apply our expertise in metabolomics in food safety, food quality and agriculture to develop and implement client-targeted solutions which address a range of biological challenges. We use a combination of advanced lab-based analytical techniques and computational and statistical methods to ensure the validity of our data and provide a detailed description of a biological system.
This involves the application of a range of imaging and screening techniques with the aim of capturing a wide range of metabolites and identifying early biomarkers for a variety of pathogens and physiological disorders in crops. Depending on the nature of the biological problem, we apply both targeted and untargeted approaches. These multivariate and high throughput datasets are used to create metabolite-based statistical models that can predict difficult-to-quantify variables or classify unknown samples. Our analytical approaches include:
- Machine Learning and Pattern Recognition algorithms for the interpretation of metabolomic data;
- Data integration and modelling for systems biology;
- Development of decision making tools and prediction models for food safety and freshness profiling.
