Spectral data compression and informative variable selection are the research focus on the application of NIR, which enable to simplify the model and improve the accuracy of prediction. The research used the pretreatment methods such as the second derivative, normalization and orthogonal signal correction (OSC) to filter irrelevant array according to the concentration of soluble solid content (SSC) based on the Vis/NIR spectroscopy of apricot. SCMWPLS was used to select 880, 894-910 and 932 nm as the regions for constructing prediction PLS model with correlation coefficient (R) of 0.920, standard error of calibration (SEC) of 0.454 and standard error of prediction (SEP) of 0.470 for SSC. Besides, after conducting an independent run for 100 times, GA obtained the regression variables as 888 and 900 nm according to the higher frequency of selection to set up GA.MLR prediction model, and the R, SEC and SEP were 0.905, 0.488 and 0.459 respectively. The results of the two modeling methods are both better than those of full-region PLS model. This demonstrates that OSC enables to filter irrelevant signal array according to the concentration of SSC and reduce the latent variables used for modeling. Also, SCMWPLS and GA can identify the optimal combination of information variables. These methods have a universal significance on building NIR express analysis model with low dimension and high precision.