•The integration of spectra and textural features were used to predict SSC.•The proposed combined PLS showed excellent ability based on the integration.•Effective variables of spectra and correlation improved SSC prediction performance. The objective of this study was to improve the detection accuracy of soluble solids content (SSC) of apples by integrating spectra and textural features. The spectral data were directly extracted from the region of interest (ROI) of hyperspectral reflectance images of apples over the region of 400–1000nm, while the textural features were obtained by a texture analysis conducted on the ROI images based on grey-level co-occurrence matrix (GLCM). A new regression method called combined partial least square (CPLS) was proposed to analyze the integrations of spectra and different kinds of textural features. In this algorithm, the score matrix matrices of the spectral data and textural features were obtained by PLS analysis separately and then used together for calibration. The prediction results indicated that the CPLS model developed with the integration of spectra and correlation feature achieved promising results and improved SSC predictions compared with the spectral data when used alone. Next, stability competitive adaptive reweighted sampling (SCARS) was conducted to select informative wavelengths for SSC prediction. The CPLS model based on the integration of SCARS selected spectra and correlation gave better results than those with the full wavelength range. The correlation coefficient and root mean square errors of prediction set and validation set were 0.9327 and 0.641%, 0.913 and 0.6656%, respectively. Hence, the integration of spectra and correlation extracted from hyperspectral reflectance images, coupled with CPLS and SCARS methods, showed a considerable potential for the determination of SSC in apples.