With the aim of solving problems related to cost, and the complicated structure of the online grading and inspection system for detecting the quality of pears, the online non-destructive system was designed for inspecting and classification of the internal quality of pears. Based on the system, the effects of prediction models on the Soluble Solids Content (SSC) and firmness of pears were researched under the different moving speeds (0.3 and 0.5 m·s-1) . Collected spectra from the same position of the pear were discrepancy at different moving speeds. Due to the discrepancy in the collected spectra, adapting spectral pre-processing methods, as SG-smoothing, SG-convolution derivative, multiple scattering correction (MSC), standard normal energy transformation (SNV), Normalization, was to eliminate differences. Adopt Partial Least Squares (PLS), prediction models of SSC and hardness for Korla Pears were established at moving speeds of 0.3 m·s-1 (S1) and 0.5 m·s-1 (S2). The results showed that the established SSC prediction model at 0.5 m·s-1 was more effective than 0.3 m·s-1 by using SG-DER (Savitzky-Golay Derivative) processing spectrogram. The correlation coefficient of the prediction set, and the root mean square errors of prediction were to be 0.880 2 and 0.391 5°Brix respectively. However, when the moving speed was 0.3 m·s-1, established the SSC model, by adapting SGS (Savitzky-Golay Smooth) processing spectrogram, was more robust than at 0.5 m·s-1. Its correlation coefficient of the prediction set, and the root mean square errors of prediction were to be 0.820 2 and 0.470 8 N respectively . Afterwards two speed hybrid prediction models were established. Competitive adaptive re-weighted sampling (CARS) and Successive projections algorithm (SPA) were used to select the characteristic variables, and PLS was used to establish hardness and SSC prediction models at mixed speeds. In view of the perspective of the model effect, SPA and CARS effectively reduced the number of variables, improving the online prediction ability and processing data speed, and enhancing the robustness of the model. Using CARS to select 24 variables from a total of 501, then which established the CARS-PLS model. Establishing the SSC prediction model was more efficient, and its correlation coefficient of the prediction set and root mean square errors of prediction were calculated as 0.915 0 and 0.371 9°Brix respectively. Using SPA to select, 32 variables were selected from a set of 501, and a firmness model was established. The correlation coefficient of the prediction set and the root mean square errors of prediction were ascertained as 0.821 0 and 0.492 0 N respectively. Establishing predictive quality model at the mixing speed is more robust than at the single speed. The research showed that the different moving speeds have different effects on the fruit quality prediction models. The research provides technical support for on-line classification of fruit quality.<英文关键词Near-infrared spectroscopy;Korla pear;Different movement speeds;On-line inspection