To ensure authenticity of sesame oil, an authentication technology was proposed using ion mobility spectrometry (IMS) and chemometrics. One-class classification (OCC) methods including one-class partial least squares (OCPLS) and one-class support vector machine (OCSVM) were employed to build authentication models for sesame oil. Subsequently, an independent test set was used to validate the constructed models. Validation set of 45 adul-terated oils indicated that prediction correction rate of OCPLS model reached 95.6％(43 out of 45). Moreover, the complete set of sesame oils adulterated by sesame oil essence could be identified as counterfeit. Compared with previous studies, OCPLS model could work to identify untargeted adulteration. In conclusion, OCC method could effectively detect adulterated sesame oils containing as little as 10％other vegetable oils. This study provided a rapid screening method for adulterated sesame oil in market surveillance and a reference for developing authentication methods of other edible oils.