Liquid chromatography-mass spectrometry (LC/MS) is one of the major techniques in metabolomic studies. It is widely used to identify disease biomarkers, drug targets, and unravel complex metabolic networks. Due to the high-noise nature of the technology, especially when measuring low-abundance components in complex samples, reliable pre-processing is critical in order to maximize information retrieval from LC/MS data. We develop a set of algorithms for the processing of high-resolution LC/MS data. The major technical improvements include the adaptive tolerance level searching rather than hard cutoff or binning, the use of non-parametric methods to fine-tune intensity grouping, the use of run filter to better preserve weak signals, and the model-based estimation of peak intensities for absolute quantification and deconvolution.