The field of liquid chromatography-mass spectrometry (LC-MS)-based nontargeted metabolomics has advanced significantly and can provide information on thousands of compounds in biological samples. However, compound identification remains a major challenge, which is crucial in interpreting the biological function of metabolites. Herein, we present a LC-MS method using the all-ion fragmentation (AIF) approach in combination with a data processing method using an in-house spectral library. For the purposes of increasing accuracy in metabolite annotation, up to four criteria are used: (1) accurate mass, (2) retention time, (3) MS/MS fragments, and (4) product/precursor ion ratios. The relative standard deviation between ion ratios of a metabolite in a biofluid vs. its analytical standard is used as an additional metric for confirming metabolite identity. Furthermore, we include a scheme to distinguish co-eluting isobaric compounds. Our method enables database-dependent targeted as well as nontargeted metabolomics analysis from the same data acquisition, while simultaneously improving the accuracy in metabolite identification to increase the quality of the resulting biological information.