Four kinds of edible oils,ie.sunflower oil,soybean oil,corn oil and peanut oil from different botanical origins were characterized by gas chromatography(GC) and near infrared spectroscopy(NIR),and the discriminant analysis models were established based on the characterization data.Meanwhile,the feasibility of data level data fusion was explored.A partial least squares discriminant analysis(PLS-DA) model was constructed based on chromatographic and spectral data fusion to classify the edible oils of botanical origins.Principal component analysis(PCA) results showed that the discriminant analysis by GC was mainly based on fatty acid composition,while that by NIR was mainly based on the characterization of hydrogen contained chemical bonds in samples.The sensitivity and specificity of the data fusion model were both 1.000,and the classification error were 0.000.Thus,low level data fusion reduced average cross validation(CV) classification error,and the model exhibited a good robustness.Compared with the results of the model based on single data of gas chromatography or near infrared spectroscopy,the data fusion strategy improved the classification performance of the model.