Tissue-targeted metabonomics provides tissue specific metabolic information while still retaining the profiling approach of traditional metabonomics. Microdialysis sampling is used to generate site-specific samples of endogenous metabolites. The dialysate samples are subjected to proton NMR analysis with data analysis by principal components analysis and partial least squares regression. In this study, sample and data pretreatment methods were examined for their impact on the quality of the data analysis. Specifically, the effects of speed vacuuming, sample solubility, sample pH stability, and sample storage stability were examined. Data pretreatment methods examined included the effects of standardization and normalization to internal standards. In addition, the ability of tissue-targeted metabonomics to generate time trend data was explored and more fully characterized using principal components analysis and partial least squares regression.