Prioritizing Unknowns in Non-Target Screening of Food Samples

LCGC International spoke to Anneli Kruve of Stockholm University about non-target screening of food samples by high-resolution mass spectrometry (HRMS), the subject of her RAFA 2026 talk, "Data-Driven Strategies to Reduce Complexity in Non-Target HRMS Analysis of Food Samples." Food matrices produce thousands of chemical features, most of them unidentified, so food safety laboratories risk spending interpretation time on features of low concern. Kruve details how predicted collision cross-section (CCS) values helped to remove 16% of incorrect candidate structures while retaining 90% of correct ones, and how retention time filtering can remove more than 40% of incorrect candidates. She advises laboratories to test bioactivity and concentration prediction models on known chemicals in their own sample types before relying on them for prioritization.