Summary: | One of the most widely used strategies for metabolite annotation in untargeted LCMS is based on the analysis of MS<sup>n</sup> spectra acquired using data-dependent acquisition (DDA), where precursor ions are sequentially selected from MS scans based on user-selected criteria. However, the number of MS<sup>n</sup> spectra that can be acquired during a chromatogram is limited and a trade-off between analytical speed, sensitivity and coverage must be ensured. In this research, we compare four different strategies for automated MS<sup>2</sup> DDA, which can be easily implemented in the frame of standard QA/QC workflows for untargeted LC−MS. These strategies consist of (i) DDA in the MS working range; (ii) iterated DDA split into several <i>m/z</i> intervals; (iii) dynamic iterated DDA of (pre)selected potentially informative features; and (iv) dynamic iterated DDA of (pre)annotated metabolic features using a reference database. Their performance was assessed using the analysis of human milk samples as model example by comparing the percentage of LC−MS features selected as the precursor ion for MS<sup>2</sup>, the number, and class of annotated features, the speed and confidence of feature annotation, and the number of LC runs required.
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