Abstract: This paper was conducted to construct a fast model for predicting drip loss rate of chicken by mining different preprocessed hyperspectral information(900~1700 nm). First,the hyperspectral images of each chicken sample were collected and the spectral information within the region of interest of the images were averaged and extracted. The mean spectral data were preprocessed by baseline correction(BC),standard normal variable correction(SNV),multiplicative scatter correction(MSC) and Gaussian filter smoothing(GFS) and normalization correction(NC),respectively. Partial least squares regression(PLSR) were used to explore the quantitative relationship between the spectral information and the drip loss rate of chicken samples. Then the regression coefficient method(RC),successive projections algorithm(SPA) and stepwise regression(Stepwise) were applied to select optimal wavelengths carrying most information for the full band PLSR models. The results showed that the BC-PLSR model built with full BC spectra had better predictive performance(r_P=0.95,RMSEP=0.29%,RPD=3.07,ΔE=0.0024%). Both the SW-BC-PLSR model(r_P=0.97,RMSEP=0.24%,RPD=3.82,ΔE=0.0012%) and SW-BC-MLR model(r_P=0.97,RMSEP=0.22%,RPD=4.19,ΔE=0.0036%) built with 14 optimal wavelengths(900.6,903.8,905.5,907.1,917.0,997.7,1162.2,1272.4,1354.8,1369.6,1410.8,1425.6,1584.1 and 1695.1 nm) selected by stepwise from full BC spectra had similar good accuracy for drip loss prediction. This experiment showed that it could be potentially realized for the rapid prediction of drip loss rate in chicken based on near-infrared hyperspectral data.