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Data sets from metabonomics or metabolic profiling experiments are becoming increasingly complex, which are hard to summarize and visualize without appropriate tools. The use of chemometric tools, such as orthogonal signal correction (OSC), principal component analysis (PCA), partial least squares to latent structure discriminant analysis (PLS-DA), and OSC-PLS-DA make the data dimensionality reduction and interpretation much easier. Here we showed a system method based on PCA, OSC-PLS-DA for metabonomic data analysis; Furthermore, the loading plots of mass data were used for the biomarkers discovery. As an example, dataset from Liu Wei Di Huang Pills administrated rats urine collected by LC/MS/MS was used to demonstrate this method. The results indicate that PCA combined with OSC-PLS-DA was a time-saving tool for data interpretation and biomarkers discovery.