Selection of Regions of interest (ROIs) and subsequent spectral extraction was a key step of non-destructive detection and analysis based on hyperspectral imaging (HSI). For the rapid and accurate detection of mutton pH, the study on the effects of 2 different ROIs on mutton pH models was carried out in the visible-near infrared region of 473～1　000 nm. 2 ROIs methods of Rectangle Regions (RR) and Image Segmentation (IS) were adopted to extract 122 corresponding representative spectra respectively. The influence of different preprocessing methods and ROIs methods on 3 pH models, including stepwise multiple linear regression (SMLR), principal component regression (PCR) and partial least squares regression (PLSR), was compared and analyzed. The results indicated that SMLR and PLSR model performance was optimal in 3 models established with spectral data extracted from Rectangle Regions (RR) and Image Segmentation (IS) respectively. As for the SMLR model, corresponding to the RR ROIs method, the correlation coefficient (Rcal) and root mean square error (RMSEC) of calibration set was 0.85 and 0.085 respectively, and the correlation coefficient (Rp) and root mean square error (RMSEP) of prediction set was 0.82 and 0.097 respectively. As for the PLSR model, corresponding to the IS ROIs method, the correlation coefficient(Rcal) and root mean square error (RMSEC) of calibration set was 0.95 and 0.050 respectively, and the correlation coefficient (Rp) and root mean square error (RMSEP) of prediction set was 0.91 and 0.071 respectively. By comparing the modeling results of spectral data extracted from 2 ROIs methods, the modeling performances of Image Segmentation (IS) were always better than Rectangle Regions (RR) in all the 3 modeling methods. The study shows that it is feasible to apply hyperspectral imaging technology combined with the ROIs method of Image Segmentation (IS) to accurate, fast and non-destructive detection of mutton pH.