Hyperspectral imaging combined with variable selection methods was used to perform the rapid and accurate detection and visualization of total volatile basic nitrogen content in mutton. For each sample, several spectra were extracted from the region of muscle pixels for modeling, and the model performance was improved and better than the model established with average spectrum extracting from each sample. By two steps of variable screening with competitive adaptive reweighted sampling and stepwise regression methods, the efficient dimensionality reduction of spectral data was achieved, and the important characteristic variables were selected. The nonlinear model significantly improved the model predictive capability, and the visualization distribution map based on the model was consistent with the actual change of meat spoilage.