It is crucial to ensure the quality stability of rosin material since the different chemical constituents of various kinds of rosins will obviously influence the quality of down-stream product. Herein, a method based on near infrared spectroscopy and partial least squares discriminant analysis was proposed to discriminate oleoresins from Pinus msssoniana and Pinus elliottii, which could be helpful to identify the species of oleoresins during the purchasing process. Eighty-two oleoresin samples from six different producing areas of Guangxi, i. e. Wuming, Fangcheng, Fuchuan, Wuzhou, Baise and Leye, were collected to develop classification model. These collected samples were consisted of 51 Pinus msssoniana and 31 Pinus elliottii. Diffuse reflection modes were applied to obtain near infrared spectrum range from 900～1 700 nm. Then, several chemometrics techniques such as sub-window permutation analysis and repeated double cross validation were used to select optimal variables and the number of principal component. Finally, 300 variables were extracted from the original variable pool and the optimal number of principal component was set to 8. Results showed that the obtained model can accurately discriminate oleoresins from Pinus msssoniana and Pinus elliottii, and the classification accuracy of external test is 96.30%, which can meet the need of quality control. The proposed method is less time-consuming, easy to operate and low-cost, and it is suitable for the quality control of purchasing process.