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With the development of social economy and growth of people’s living standand, the demond of fruit quality is ever increasing. Quality detection and grading of postharvest fruit is an integral part of commoditization processing, which is also an effective way to achieve high price with good quality. Visible/NIR spectroscopy with the advantages of rapid, nondestructive and being on-line analyzing, has been widely used in agriculture. In the actual application of visible/NIR spectroscopy for on-line detection of fruit internal quality, multi-channels measurement often exists, in which the prediction model is not universal among multi channels due to different spectrometers or their different manufacture precisions. Calibration model transfer is a key problem in visible/NIR spectral quantitative analysis. Comparative analysis of some calibration model transfer methods, such as direct standardization (DS), piecewise direct standardization (PDS), slope/bias (S/B) between two different visible/NIR spectrometers (master and slave spectrometers, model QE65000 and QE65Pro, Ocean Optics, Inc., USA) in the sugar content on-line detection of crown pears was carried out at conveyor speed of 0.5m/s. The results showed that the prediction values by DS algorithm and DS algorithm based on the mean spectra subtraction correction (MSSC-DS) were relatively good with low root mean square error of prediction (RMSEP) of less than 0.5°Brix, which can satisfy the industry application. And pre-processing method of MSSC can improve the prediction accuracy of calibration model transfer by eliminating and mitigating the differences between the spectra acquired on master and slave spectrometers. However, the best prediction result on salve instrument after calibration model transfer (RMSEP was 0.453°Brix) was still inferior to that predicted by the model developed directly using slave data (RMSEP was 0.381°Brix). Thus, in the actual application, appropriate modeling selection should be considered from the cost and the accuracy of classification.