It’s common in edible oil market that adulterating low price oils in high price oils. Sesame oil was often adulterated because of its high quality and price, so the authentication and adulteration of sesame oil were qualitatively and quantitatively analyzed by Fourier transform infrared (FTIR) spectroscopy combined with chemometrics. Firstly, FTIR spectra of sesame oil, soybean oil, and sunflower seed oil in 4 000～650 cm-1 were analyzed. It was very difficult to detect the difference among the spectra of above edible oils, because they are all mixtures of triglyceride fatty acids and have similar spectra. However, the FTIR data of edible oils in the fingerprint region of 1 800～650 cm-1 differed slightly because their fatty acid compositions are different, so the data could be classified and recognized by chemometric methods. The authenticity model of sesame oil was built by principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA). The recognition rate was 100%, and the built model was satisfactory. The classification limits of both soybean oil and sunflower seed oil adulterated in sesame oil were 10%, with the chemometric treatments of standard normal variation (SNV), partial least square (PLS) and PCA. In addition, the FTIR data processed by PCA and PLS were used to establish an analysis model of binary system of sesame oil mixed with soybean oil or sunflower oil, the prediction values had good corresponding relationship with true values, and the relative errors of prediction were between －6.87% and 8.07%, which means the quantitative model was practical. This method is very convenient and rapid after the models have been built, and can be used for rapid detection of authenticity and adulteration of sesame oil. The method is also practical and suitable for the daily analysis of large amount of samples.