Hyperspectral imaging can obtain both the image information and spectral information of the detected object, and make qualitative and quantitative analysis with internal components. The research on meat quality by hyperspectral imaging technology focuses on water, total viable count, color, pH, total volatile basic nitrogen etc. There are few studies on meat tenderness based on interval variable iterative space contraction method. In this paper, tenderness values of chilled Tan-sheep were detected non-destructively by visible-near-infrared and near-infrared bands (400~1 000, 900~1 700 nm) combined with chemometric methods to obtain the best modeling bands. Firstly, hyperspectral images of lamb sample were collected and extracted the spectral values of the region of interest; lamb tenderness was measured using a TA-XTplus texture analyzer; Secondly, the original spectral data between 400～1 000 and 900～1 700 nm were preprocessed by multiple scattering correction (MSC), de-trending, standard normal variate (SNV), baseline, normalize, Savitzky-Golay; optimal bands were selected by successive projection algorithm (SPA), competitive adaptive reweighted sampling (CARS), variables combination population analysis (VCPA) and interval variable iterative space shrinkage approach (IVISSA). Finally, optimal bands were selected and partial least squares regression (PLSR) model of chilled mutton was established. The results showed that: (1) the prediction performance of original spectral model in the region of 900～1 700 nm was better than that of 400～1 000 nm. (2) original spectral mode of the tenderness of chilled Tan-sheep had the best performance Rc=0.83, Rp=0.79, RMSEC=874.94, RMSEP=1 465.97 in the near-infrared region using different pretreatment methods. (3) the original spectrum of 900～1 700 nm was selected by SPA, CARS, VCPA and IVISSA with 15, 16, 13 and 123 characteristic wavelengths, accounting for 7%, 6%, 5% and 54% of the total wavelength. (4) the prediction model of chilled Tan-sheep tenderness was the best in combination with hyperspectral technique and OS-IVISSA-PLSR with Rc=0.85, RMSEC=850.86, RMSECV=1 193.42, Rp=0.79, RMSEP=1 497.11. It was indicated that IVISSA algorithm could greatly reduce the number of model operations and ensure the predictability and stability of the model. It is feasible to adopt OS-IVISSA-PLSR method to analyze the tenderness of chilled Tan-sheep mutton.