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Data sets in metabonomics or metabolic profiling experiments are always multidimensional, which brings some difficulties to metabonomics reaearchers. Generally, chemometric tools, such as orthogonal signal correction (OSC), principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), (orthogonal partial least squares discriminant analysis (OPLS-DA) are introduced to the metabonomics, which can easier the data dimension reduction and interpretation. Here PCA, OSC-PLS-DA as an system method for metabonomic data analysis was shown; what is more, a visualized tool based on OSC-PLS-DA, U-plot, was used for the biomarkers discovery. As an example, dataset from Ginger water extract administrated spleen deficiency rats plasma collected by LC/MS/MS was used to demonstrate this method. As a result, PCA was an useful tool for metabonomic dataset dimension reduction, OSC is an powerful tool for data filteration, U-plot based on OSC-PLS-DA was proved to be an effective, time saving tool which can help metabonomics data interpretation and biomarkers discovery. In conclusion, the a system method shown by this paper is suitable for the matabonomic study.