Sustainable use of AI-based electronic noses in the life science field in changing environments

Researcher
Julius W├Ârner
Collaboration
Alaa Othman
Research Theme
R2 Prosilience & Robustness

The project integrates an electronic nose (e-nose) in various life science use cases in order to advance the sustainable and intentional use of artificial intelligence in this research field. Methodically, relevant volatile substances found in life science applications are analyzed in a simplified environment in the laboratory via an e-nose and processed with machine learning algorithms to assess the applicability in real processes. In addition to the focus on discrimination, the regression and prediction of concentrations of certain key volatile markers also play a role. For example, the project is also looking at the production of beer and the monitoring of various key substances. The lowest concentration at which several volatile aromas in beer can be detected is analyzed to determine whether an e-nose would be suitable for process control and quality assessment. Central problems such as data drift, low concentrations of the target substance or small data sets are major problems of all use cases that need to be solved using suitable algorithms. This methodical approach of solving sub-problems of different applications provides valuable input to address our long-term goal of diagnosing wound infections with e-noses.