To determine whether Fourier-transform infrared (FT-IR) spectral analysis based on multivariate analysis for whole-cell extracts can be used to discriminate different countries of Perilla seeds at the metabolic level, leaves of Perilla seeds were subjected to FT-IR spectroscopy. FT-IR spectral data of leaves were analyzed by principal component analysis (PCA), partial least square discriminant analysis (PLS-DA), and hierarchical clustering analysis (HCA). FT-IR spectra confirmed typical spectral differences between frequency regions of 1,700-1,500, 1,500-1,300, and 1,100-950 cm^-1. These spectral regions reflect the quantitative and qualitative variations of amide I, II in amino acids and proteins (1,700-1,500 cm^-1), phosphodiester groups in nucleic acids and phospholipids (1,500-1,300 cm^-1), and carbohydrates (1,100-950 cm^-1). PCA revealed separate clusters corresponding to their country relationship. Thus, PCA could be used to distinguish between countries of origin with different metabolite contents. And PLS-DA showed a similar country classification of Perilla seeds. Furthermore, these metabolic discrimination systems could be used for the rapid selection and classification of useful field crop cultivars.