A rapid nondestructive method for identifying intact foods containing trans fatty acids (TFA) using diffuse near infrared spectroscopy (NIR) was proposed in the present paper. The diffuse Fourier transform near infrared (FT-NIR) spectra of intact samples were collected by fiber probe, and the reference data of TFA content were determined by Chinese standard method GB/T 22110—2008 (gas chromatography (GC) method). In this work, all the samples were classified into two categories: foods with TFA and foods without TFA according to the TFA content of the foods. The identification models were established by different supervised pattern recognition algorithms including partial least square discriminant analysis (PLSDA), support vector machine (SVM), soft independent modeling of class analogy (SIMCA) and K-nearest neighbor method (KNN) etc. The performances of the established models employing different algorithms, data pretreatments and wavelength bands were compared. The results show that PLSDA and SVM algorithms have the ability of identifying intact foods with TFA, and the performance of identification models established by PLSDA is better than that of SVM. The PLSDA models established by the wavelength bands of 4 138～4 428, 5 507～5 963 and 7 794～8 960 cm-1 which were pretreated with pretreatment methods of auto scaling and second derivative have the best performance. The correct classification percentages of its calibration and validation set are 96.4% and 88%, respectively, which indicates that this method is feasible for the identification of foods with TFA. This NIR method above mentioned has the characteristics of rapidness, non-destruction and easy operation due to the elimination of sample pretreatment such as oil extraction and grinding, therefore it is very suitable for on-line and in-site detection application.