Urinary mercapturic acids represent valuable biological markers of chemical exposure and detoxification mechanisms. Characterization of this class of compound has historically employed LC-MS/MS analytical platforms using negative ion mode. In this study, we report the first application of a UHPLC-MS/MS method using positive ion mode detection for the unbiased characterization of mercapturic acids. A preliminary spectral library of synthetically available mercapturic acids was generated to evaluate fragmentation pathways of mercapturic acids in positive mode. From our findings, we propose a discovery method that utilizes a neutral loss monitoring paradigm based on two diagnostic fragmentation pathways of mercapturic acids. Using a cohort of 20 nonsmokers and 20 smokers, we detected 180 putative mercapturic acid signatures that exhibited a high degree of reproducibility. Following a combination of multivariate and univariate statistics, we found 33 putative mercapturic acids associated with smoking status. The increased structural insights of analytical profiling in positive mode enable more informative annotation of discovery results. Using the latest structural prediction technology, we were able to assign preliminary structural identifications to these features and eliminate likely false positive detections. From our workflow, we discovered a previously unreported mercapturic acid with a strong association with tobacco cigarette usage and putatively identified it as