Human biomonitoring assesses actual internal contamination of chemicals by measuring exposure markers,chemicals or their metabolites,in urine,blood,serum,or other body fluids.The metabolism of chemicals within an organism is complex; therefore complete identification of metabolites is often challenging.Several untargeted metabolomics methods have been developed to perform objective searching/filtering of accurate-mass-based LC-MS data to facilitate metabolite identification.In this study,three metabolomics data processing approaches were used for exposure marker discovery with an LTQ-Orbitrap high-resolution mass spectrometry(HRMS)dataset; di-isononyl phthalate(DINP)was used as an example.The data processing techniques included SMAIT,mass defect filtering(MDF),and XCMS.Among 14 probable metabolite signals found,13 were validated as exposure-related markers in a rat model.With the use of HRMS,we found three novel metabolites(m/z 279.123,293.139 and 321.170)with different m/z values than those reported in the literature(m/z 279.087,293.103 and 321.134,respectively).The m/z values derived from the three hydroxyl-metabolites are only slightly different(0.036 u)from those containing carboxyl or carbonyl groups.The finding also raises subtle implications on quantitation of urinary exposure markers by mass spectrometers with a diverse mass accuracy being used by a wide range of analytical laboratories.Triple quadrupole mass spectrometers(TQMS)are frequently used for routine quantitative analyses for urinary exposure markers.However,they may not be able to differentiate metabolites with a small mass variance.While HRMS,such as TOF and Orbitrap,they may not measure correctly the concentrations if an incorrect chemical structure is assumed.