Total Volatile Basic Nitrogen (TVB-N) was usually taken as the physicochemical reference value to evaluate the mutton freshness. In order to explore the feasibility of hyperspectral (HSI) imaging technique to detect mutton freshness, 71 representative mutton samples were collected and scanned using a diffuse reflectance hyperspectral imaging (HSI) system in the Visible-Near infrared (NIR) spectral region (400～1 000 nm), and the chemical values of TVB-N content were determined using the semimicro Kjeldahl method according to the modified Chinese national standard. The representative spectra of mutton samples were extracted and obtained after selection of the region of interests (ROIs). The samples of calibration set and prediction set were divided at the ratio of 3∶1 according to the content gradient method. Optimum HSI calibration models of the mutton (TVB-N) were established and evaluated by comparing different spectral preprocessing methods and modeling methods, which included Stepwise Multiple Linear Regression (SMLR), Partial Least Squares Regression (PLSR) and Principal Component Regression (PCR) methods. The results are that through the utilization of Multiplicative Scatter Correction (MSC), first derivative, Savitzky-Golay (S-G) smoothing and mean-centering together, both PLSR and PCR were able to achieve quantitative detection of mutton TVB-N. As for the PLSR model of mutton TVB-N established, the spectral pretreatment methods chosen included MSC, first derivative, S-G (15,2) smoothing and mean-centering, and the latent variables (LVs) number used was 11. As for the calibration set of PLSR model of mutton TVB-N, the correlation coefficient (r) and root mean square error of calibration (RMSEC) were 0.92 and 3.00 mg·(100 g)-1, respectively. As for the prediction set of PLSR model of mutton TVB-N, the correlation coefficient (r), Root Mean Square Error of Prediction (RMSEP), and ratio of standard deviation to standard error of prediction (RPD) were 0.92, 3.46 mg·(100 g)-1 and 2.35, respectively. The study demonstrated that the rapid and accurate analysis of TVB-N, the key freshness attribute, could be implemented by using the hyperspectral imaging (HSI) technique. The study provides the basis for further rapid and non-destructive detection of other mutton freshness attributes by using the hyperspectral imaging (HSI) technique, the improvement of current modeling effect of TVB-N content and the application involved of the technique in the practical production.