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When using visible/near-infrared diffuse reflectance spectroscopy for the detection of soluble solids content（SSC）in apples, the distance from the spectral acquisition probe to the sample surface varies randomly and uncontrollably, resulting in a reduction of detection accuracy. Moreover, when using characteristic wavelengths to establish the prediction models, the contribution of non- characteristic wavelengths to the prediction results is often neglected, resulting in the loss of spectral information. Therefore, a distance correction（DC）method was proposed by exploring the law of the influence of detection distance on diffuse reflectance spectra and establishing prediction models for apple SSC by combining the modeling method of fusion of characteristic wavelength and non-characteristic wavelength data. The results showed that DC could more effectively improve the prediction performance of the PLSR model;the use of the competitive adaptive reweighted sampling （CARS ） algorithm for characteristic wavelength screening based on DC preprocessing could effectively simplify the model and improve the model prediction performance; and the fusion modeling results of characteristic and non-characteristic wavelength data of the CARS algorithm had the best prediction performance, with the correlation coefficient of calibration（ Rc）, root mean square error of calibration（RMSEC）, the correlation coefficient of prediction（Rp）, root mean square error of prediction（RMSEP）and relative percentage difference（RPD ）of 0.981, 0.297%, 0.957, 0.469% and 3.424, respectively.