Adulteration of edible vegetable oils is a very serious problem affecting its commercial value and customers’ health. FT-NIR and Confocal Micro-Raman have been explored for discriminating adulteration of sesame oils. The soybean, corn, and peanut oil were mixed into sesame oils in the range of 5~20% (v/v),respectively,and their Confocal Micro-Raman and FT-NIR spectra were collected. Principal component analysis (PCA) and Partial least squares discriminant analysis (PLSDA) were used for the discrimination and classification of adulteration in sesame oil based on spectral data. In addition, the spectra were subjected to the pretreatements (eg, Savitzky-Golay smoothing, Multiplicatnive Scatter Correction, first derivatives and second derivatives) before developing PCA and PLS-DA models. FT-NIR was found to give better efficient in classification of adulteration oils by using PLS-DA with ca.100% classification accuracy, and Raman also gave 100% classification accuracy. The results demonstrated that FT-NIR and Confocal Micro-Raman both can be used to determine the authenticity of edible oils rapidlly.