Abstract: Abstract: By using low field nuclear magnetic resonance (LF-NMR) spectrometer, the leisure dried tofu samples were measured for obtaining transverse relaxation data. The experimental data were analyzed by pattern recognition methods including principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and Bayesian regularization-back propagation-artificial neural network (BR-BP-ANN). The research purpose is to search for a method which can quickly discriminate the brand of leisure dried tofu. The 4 common dried tofu brands were chosen and the samples of each brand were collected from 5 batches of leisure dried tofu. Sixteen small bags of dried tofu were randomly selected as study samples from each batch. In this way, 320 samples were obtained. These samples were measured with an LF-NMR instrument. Each sample was measured repeatedly 3 times. The average value of them was taken as the result of the measurement. A total of 320 sample spectra were obtained. Then, 60 samples were selected randomly as training set from 80 samples of each brand. A total of 240 training samples were randomly acquired in this manner, and the remaining 60 samples were used as prediction set. These samples of 4 brands were used for rapid distinction by pattern recognition methods. The experimental results showed that each brand was difficult to be picked out with eyes by three-dimensional PCA scoring plot. Further, measurement data of dried tofu were treated by PLS-DA. The results displayed that the recognition rate of Brand 4 was the highest, 93.3%, and the worst was the recognition rate of Brand 3, 76.7%, and the total recognition rate of all brands was 86.3% for the training set. For the prediction set, the recognition rate of Brand 4 was also the highest, and the recognition rate of Brand 2 was the worst, 75%, and the total recognition rate was 81.3% for all brands. However, the BR-BP-ANN method can discriminate 4 bands simultaneously. The predicted value is in good agreement with the actual expected value for training set. In other words, the prediction values were highly consistent with the expected values of 1-0-0-0 for No. 1-60 samples, which met the criteria. So these samples belonged to Brand 1. Similarly, the prediction values were in agreement with the expected values of 0-1-0-0 for No. 61-120 samples. Then these samples were classified into Brand 2. In the same way, No. 121-180 samples were taken into account, and the prediction values were close to the expected values of 0-0-1-0. Consequently, these samples belonged to Brand 3. Similarly, No. 181-240 samples belonged to Brand 4 since the prediction values were similar to the expected values of 0-0-0-1. In the same way, good results were also obtained for prediction set since the predicted values were in perfect agreement with the expected values. It's interesting to note that the correct rate of prediction was 100% for brand discrimination. So the combination of LF-NMR and BR-BP-ANN can provide a fast and accurate method and better technical support for the brand discrimination of the leisure dried tofu.