The research was to detect soluble solids content (SSC) of pear online by near infrared transmission spectrum. The movement speed of pear was 0.5 m x s(-1) the power of light source was 300 W, and semi-transmission was used to collect the spectrum of pears. The total experiment samples were 187 pears, with a calibration set of 147 pears and a validation set of 40 pears. Partial least squares (PLS) and principal component regression (PCR) technique were used to develop the calibration model for online detection. Spectral ranges of 550-700 nm, 700-850 nm, 550-850 nm were used to establish the calibration models, and it was found that the model with 550-850 nm was better than others whether for PLS or for PCR. Also, the models based on different pretreatment methods such as Savitzky-Golay smooth, first derivative, second derivative and so on were compared, and the result showed that the five point Savitzky-Golay smooth could increase S/N ratio and improve performance of the model, whereas first derivative and second derivative could do little to improve performance of the model. The best model had the satisfactory calibration and prediction abilities, with the correlation coefficient (Rc) = 0.948 8, root mean square error of calibration (RMSEC) = 0.236 and root mean square error of validation (RMSEP) = 0.548. The result in this study shows that the detection of SSC of pear online is feasible.