In order to realize real-time, on-line and non-destructive evaluation of main freshness attributes of raw meat, an on-line detection system based on dual-band visible/near-infrared reflectance spectroscopy(350~1100 and 1000~2500 nm) was established in this paper. The hardware which includes the light source unit, the spectrum acquisition unit, the control unit and the driving unit was designed. The light source fixing support and installation angle were optimized, and the corresponding control program was developed. Based on those, two sets of on-line detection systems were developed for laboratory use and to satisfy the demands of different production lines. Firstly, the experimental parameters including the conveyor speed and the distance between sample surface and the entrance of the lens were optimized. By comparing the spectral similarity and the significance analysis, the conveyor speed and the distance were determined as 275 mm·s-1 and 12 cm to obtain stable spectra. Then, based on the experimental parameters, the reflectance spectra of 50 pork samples stored for 1~13 days were collected under static and on-line conditions, respectively. The dual-band spectra were fused by parabolic fitting to obtain a complete spectrum which covered the whole visible and near-infrared region. Subsequently, all spectra were rearranged at 2 nm intervals by means of cubic spline interpolation to make the spectral data points distribute evenly over the two bands. Based on this, the spectrum was smoothed by the moving window polynomial fitting least square method and normalized by standard normal variable transformation. Then the prediction models for L*, a*, b*, pH and total volatile base nitrogen under static and on-line conditions were established and compared to verify the reliability of the constructed system. It was found that the modeling results for on-line detection performed worse than those under static conditions, and the reason may be attributed to the spectrum drift. Therefore, first derivative was further employed to eliminate the baseline drift and enhance the band characteristics. The influence of processing sequence of first derivative and standardization on the modeling results was also discussed. The results showed that the first derivative followed by standardization worked more successfully to eliminate the external interference. Then the prediction models for L*, a*, and b* were established based on the first band, and the models for pH and total volatile basic nitrogen were established based on the dual-band spectrum, with correlation coefficients of 0.955 3, 0.924 7, 0.955 1, 0.961 5 and 0.966 8. Finally, 20 independent samples were detected using the developed on-line inspection system to verify the model applicability, and the correlation coefficients for L*, a*, b*, pH and total volatile basic nitrogen were 0.918 9, 0.914 1, 0.947 7, 0.950 4 and 0.960 6, respectively. The results showed that by the real-time acquisition and fusion of dual-band spectroscopy, more optical signal were collected to reflect the internal information of tested samples. Combined with the designed optical path and other hardware units, the spectral information within a larger area of the sample surface were obtained. Thus, the non-destructive, online, and real-time assessment of the main attributes for raw meat freshness was achieved. The system was easy to assemble and disassemble, which made it possible to satisfy the actual needs of different production lines and had strong practical value and market prospects.