This study investigated the potential of using time series-hyperspectral imaging (TS-HSI) in visible and near infrared region (400-1700 nm) for rapid and non-invasive determination of surface total viable count (TVC) of salmon flesh during spoilage process. Hyperspectral cubes were acquired at different spoilage stages for salmon chops and their spectral data were extracted. The reference TVC values of the same samples were measured using standard plate count method and then calibrated with their corresponding spectral data based on two calibration methods of partial least square regression (PLSR) and least-squares support vector machines (LS-SVM), respectively. Competitive adaptive reweighted sampling (CARS) was conducted to identify the most important wavelengths/variables that had the greatest influence on the TVC prediction throughout the whole wavelength range. As a result, eight variables representing the wavelengths of 495 nm, 535 nm, 550 nm, 585 nm, 625 nm, 660 nm, 785 nm, and 915 nm were selected, which were used to reduce the high dimensionality of the hyperspectral data. On the basis of the selected variables, the models of PLSR and LS-SVM were established and their performances were compared. The CARS-PLSR model established using Spectral Set I (400-1000 nm) was considered to be the best for the TVC determination of salmon flesh. The model led to a coefficient of determination (rP(2)) of 0.985 and residual predictive deviation (RPD) of 5.127. At last, the best model was used to predict the TVC values of each pixel within the ROI of salmon chops for visualizing the TVC distribution of salmon flesh. The research demonstrated that TS-HSI technique has a potential for rapid and non-destructive determination of bacterial spoilage in salmon flesh during the spoilage process.