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. Author manuscript; available in PMC: 2022 Mar 8.
Published in final edited form as: Anal Chim Acta. 2021 Jan 12;1149:338210. doi: 10.1016/j.aca.2021.338210

Fig. 3.

Fig. 3.

Integrating on-the-fly filtering and DDA effectively reduces the MS/MS burden by 90% during acquisition of an untargeted metabolomic dataset without sacrificing selectivity or coverage of unique biological metabolites. (A) Number of features detected as a function of retention time for E. coli analyzed with a ZIC-pHILIC method. The total number of features at any given time is substantially higher than the number of features that pass filtering or credentialing analysis. (B) The three-step workflow described selects a small fraction (~10%) of the total number of features detected in the E. coli experiment. (C) Ninety-two percent of the biological metabolites determined to be unique by isotope-based credentialing were included within the subset selected during data acquisition. All but one were fragmented after four analytical runs.