The research discussed the prediction method of apple’s internal quality such as firmness and soluble solids content with the combination of parameters getting from hyperspectral fitting scattering curve. The research compared different molding methods using the combination of the three Lorentzian fitting parameters with partial least squares (PLS), stepwise multiple linear regression (SMLR) and neural network (NN). The normalized combination parameters and original combination parameters were used to establish prediction models, respectively. The partial least squares (PLS) prediction models using the combination of three original parameters gave a better results with the correlation of calibration Rc=0.93, the standard error of calibration SEC=0.56, the correlation of validation Rv=0.84, and the standard error of validation SEV=0.94 for firmness of apples. The partial least squares (PLS) prediction models using combination of normalized parameters also gave a good results with Rc=0.95, and the standard error of calibration SEC=0.29, the correlation of validation Rv=0.83, and the standard error of validation SEV=0.63 for soluble solids content of apples. The research showed that using hyperspectral scattering curve can detect apple quality attributes at the same time.