It is of great importance to detect soluble solids content (SSC) of online testing in hybrid “Skiranui Tangerine” citrus by using near-infrared diffuse transmittance spectra. In order to lay a good foundation for accurate and rapid online classification, this study focuses on the influence of variable methods on soluble solids content in hybrid “Skiranui Tangerine” citrus. We selected the random shape hybrid “Skiranui Tangerine” citrus with segments inside as the research object. In spectral range of 560~930 nm, the calibration models were developed based on partial least squares (PLS) in this experiment. Firstly, different pretreatment methods such as Savitzky-Golay, the first derivative and so on were compared with PLS Modeling results. Then moving window partial least squares (MWPLS), genetic algorithm (GA) and successive projections algorithm (SPA) were employed to improve the predictive models. After comparing the results, light scattering can be effectively eliminated by the multiplicative scatter correction (MSC). Moreover, fewer variables and model optimization were carried out with GA. The best calibration model obtained with GA-PLS method had the correlation coefficient of prediction (RP) of 0.956, the root mean square errors of prediction (RMSEP) of 0.380, the correlation coefficient of calibration (RC) of 0.967 and the root mean square errors of calibration (RMSEC) of 0.340. The experiment showed that online detection of SSC of “Skiranui Tangerine” is completely feasible.