Soluble protein and glutathione (GSH) are important physiological and biochemical indicators of mutton, which are also significant in measuring the body’s antioxidant capacity. However, the traditional detection methods are complicated and time-consuming. This report applied visible-near-infrared (400~1 000 nm) hyperspectral imaging technology to achieve nondestructive and rapid detection of soluble protein and glutathione (GSH) content in mutton. Four methods are used to preprocess the original spectral information of the collected 180 mutton samples, and then use the competitive adaptive weighting algorithm (CARS), the wavelength space iterative shrinkage algorithm-iteration and retained information variable method (iVISSA-IRIV) method for characteristics band extraction. At the same time, the gray level co-occurrence matrix method (GLCM) is used to extract the texture information of the principal component image with the highest contribution rate. Finally, the optimized preprocessing method and the characteristic wavelength information are combined with multiple linear regression (MLR), and least squares support vector machine (LS-SVM) prediction models respectively, as spectral information and spectral-texture fusion information, to establish the prediction models of soluble protein and glutathione content of mutton. The results illustrate that the PLSR model of mutton soluble protein content established by the original spectrum without pretreatment has the best effect, and its R_c and R_p are 0.875 7 and 0.854 7, respectively; the PLSR model of mutton GSH content established by the spectra after pretreatment with SNV method work best, with R_c and R_p of 0.804 8 and 0.826 5, respectively. A total of 31 characteristic wavelengths were screened using iVISSA-IRIV, and the R_c and R_p of the established mutton soluble protein LS-SVM model were 0.914 6 and 0.881 8 respectively, which are the best. The meanwhile, 29 characteristic wavelengths were screened using iVISSA-IRIV, and the R_c and R_p of the established mutton GSH-MLR model were optimal, 0.844 6 and 0.870 5, respectively. The comparison of the spectral feature information and the fusion model of the map information revealed that the establishment of the iVISSA-IRIV-LS-SVM model was the best for the prediction of soluble protein in mutton, with R_c and R_p of 0.914 6 and 0.881 8, respectively. The MLR model established by fusion of the spectral feature information extracted by SNV-iVISSA-IRIV method with the texture information is the optimal model for predicting the GSH content of mutton, and its R_c and R_p are 0.849 5 and 0.890 4, respectively. The optimal iVISSA-IRIV-LS-SVM and iVISSA-IRIV-MLR models and imaging processing methods visually represented the spatial distribution of soluble protein and GSH contents of mutton samples in combination with pseudo-color images. The current study demonstrated that the spectral and textural information from hyperspectral images could predict soluble protein and GSH content of mutton.