1. Predictive Food Effector Systems
After harvesting of raw materials and throughout processing into the final food products, the ingredients of plant and animal-derived foods undergo complex cascades of chemical and biochemical conversions. The scientists of the institute study the fate of functional effector systems from raw materials via food products on their way to the consumer in order to better understand and predict the molecular changes along the entire value-added chain.
Supported by informatics tools and machine learning concepts, they engineer those functional effector systems, that largely determine individual preferences (e.g. aroma, taste), the acceptance (e.g. food hypersensitivities), and the nutritional value of foods (e.g. immune stimulation).
- Structure-function understanding of complex patterns of technofunctional and biologically relevant molecular effector systems in foods.
- Understanding and control of the transformation of relevant food effector systems along the supply chain.
- Knowledge creation and prediction of food effector systems to meet individuals‘ preferences, acceptance and physiological needs by using the potential of new varieties, alternative raw materials (algae etc.), process side streams and next-generation production and process technologies.
- Building the molecular basis for digital food twins to master future value chains responsive to on-demand food production, and to secure traceability & auditability of food quality, safety, and authenticity (Food Industry 4.0).