Anneli Kruve leads a research group focused on modeling and machine learning techniques to understand ionization processes in electrospray ionization (ESI). Her group is dedicated to developing semi-quantitative non-targeted analysis methodologies which are crucial in various branches of analytical chemistry, including environmental analysis, metabolomics, and monitoring food contaminants. Kruve's expertise lies in mass spectrometry, particularly in the use of methods that guarantee low detection limits and high selectivity. Despite the advantages of mass spectrometry, she acknowledges the challenges of targeted analysis, which is often tedious and limited in scope due to the need for standards to evaluate the vast differences in ionization efficiencies of compounds. Her research aims to overcome the bottlenecks in non-targeted metabolomics, focusing on developing novel strategies for non-targeted quantification by leveraging conventional liquid chromatography-mass spectrometry techniques integrated with machine learning approaches. These methods have been applied to various number applications, including the detection of emerging contaminants in water and metabolites in cell cultures.
Stockholm University • Stockholm, Sweden
Leads a research group focused on machine learning and non-targeted analysis methodologies in analytical chemistry.