Background ions into exclusion list: A new strategy to enhance the efficiency of DDA data collection for high-throughput screening of chemical contaminations in food

Abstract

Foods contaminated with hazardous compounds, could pose potential risks for human health. To date, there is still a big challenge in accurate identification. In this study, a novel data-dependent acquisition (DDA) approach, based on a combination of inclusion list and exclusion list, was proposed to acquire more effective MS/MS spectra. This strategy was successfully applied in a large-scale screening survey to detect 50 mycotoxins in oats, 155 veterinary drugs in dairy milk, and 200 pesticides in tomatoes. Compared with traditional acquisition modes, this new strategy has higher detection rate, particularly at ultra-low concentration by eliminating background influence, thereby generating the MS/MS spectra for more potential hazardous materials instead of matrix interference. Additionally, the obtained MS/MS spectra are simpler and more likely to be traced back than DIA. Moreover, this new strategy would be more comprehensively applied in food safety monitoring with the improvement of HRMS and post-acquisition techniques.