A multi-mode adjustable optical mechanism was used to collect the spectra of apples in three detection methods: diffuse transmittance, total transmittance and diffuse reflection. The spectrum characteristics of apples under different detection methods were studied and PLS established the apple soluble solid content prediction model. First, the system will collect the spectra of four points on the equator of each sample under the way of diffuse transmittance, total transmittance and diffuse reflection respectively, and then MSC (Multiplicative Scatter Correction), BOC (Baseline offset correction), Normalize, Gaussian filter smoothing and other methods will be applied to preprocess the 120 averaged spectra combined with the CARS method to filter the characteristic wavelength of the diffuse reflection spectrum. Finally, PLS will be used to establish a model for predicting apple's SC , and another 30 apples will be purchased to verify the performance of the model. The results show that the spectra collected by the self-designed fruit quality detection system under the three detection methods have good result in predict SSC content in apples after 3-point Gaussian filtering smoothing pretreatment. The performance of diffuse transmittance model is R_cal=0.972, R_pre=0.967 and RMSEC=0.436%, RMSEP=0.507%. The performance of total transmittance model is R_cal=0.964, R_pre=0.957 and RMSEC=0.5%, RMSEP=0.574%. The performance of diffuse reflection model is R_cal=0.963, R_pre=0.949 and RMSEC=0.522%, RMSEP=0.536%. The fusion modeling performance of the three spectra after normalization pretreatment is R_cal=0.894, R_pre=0.857 and RMSEC=0.836%, RMSEP=0.966%. Further, the diffuse reflection spectrum is combined with the CARS algorithm to filter the characteristic wavelengths. The performance of the model established with 119 variables is R_cal=0.986, R_pre=0.977 and RMSEC=0.323%, RMSEP=0.362%. Finally, the model is imported into this new multi-mode adjustable fruit detection system, and 30 non-model apples are used to test the model to predict the performance of apple's SSC. The results show that the correlation coefficient of the 30 external validation sets is 0.906, and the root means square error of validation is 0.707%. It further shows that the diffuse reflection spectrum collected by the multi-mode adjustable fruit internal quality detection system, which is combined with spectral pretreatment, CARS and PLS can establish a better model to predict the solid soluble content of apple. This research provides new technical support for Apple's internal quality testing.