Grapefruit peel is thick. The peel and pulp belong to two different media, and the refractive index and absorption of light from the peel and pulp are different. Modeling of grapefruit soluble solids content without removing the interference of fruit peel, can lead to poor model accuracy. To address the problem of poor model accuracy due to the mismatch between spectral acquisition and target when building a fruit quality detection model. To address the problem of poor model accuracy due to the mismatch between spectral acquisition and target when establishing fruit quality detection models, this study takes ShangraoMajiya grapefruit as the experimental object, builds an adjustable experimental platform independently, acquires and analyzes the light energy attenuation pattern of whole grapefruit, searches for the relationship between grapefruit thickness and light transmission, and explores the effects of peel thickness and light transmission depth on grapefruit SSC detection accuracy. Firstly, the transmitted light source was placed directly above the equatorial circle of the grapefruit, and the spectral intensity received by different regions of the equatorial grapefruit circle was counted. The spectral intensity distribution was plotted, and the results showed that the further away from the light source emission point, the lower the spectral intensity. The light intensity received at the incident point from far and near positions accounted for 33.40%, 2.90%, 0.50%, 0.40%, 0.20%. The absorption of light by the grapefruit peel was more obvious, and the scattered light energy accounted for a smaller proportion. Secondly, the slicing method was used to record the remaining thickness and the corresponding spectral intensity, and to draw the curve of the changing pattern of spectral intensity.The smaller the remaining thickness, the greater the spectral intensity when the thickness was 32.90 mm, the spectral intensity changed dramatically, when the fruit thickness was higher than 32.92 mm, the photon intensity received by the fruit was generally lower when the fruit was lower than 32.92 mm, the spectral intensity undertook a jump increase. Then the flesh, whole fruit and peel spectra were collected, and the SSC prediction model was developed using partial least squares, and the best prediction was obtained for the peeled flesh. Finally, the spectra were collected when the thickness of grapefruit flesh, peel + flesh was 40, 30, 20 and 10 mm, and the SSC prediction models with different thicknesses were established. The correlation coefficients of the prediction sets were 0.91, 0.89, 0.87 and 0.86 when the thickness of flesh was 20, 40, 60 and 80 mm respectively. The SSC prediction model had the highest accuracy when the flesh was at a transmission depth of 20 mm. The spectral transmission depth of peel+pulp was 20, 40, 60 and 80 mm, and the prediction set correlation coefficients were 0.78, 0.86, 0.93 and 0.84, respectively, with the best prediction at a transmission depth of 60 mm for peel+pulp.The results show that the difference in tissue composition inside the fruit skin and pulp affects the results of SSC prediction, but changing the transmission distance of visible/NIR inside the fruit can also optimize the model accuracy. This study reveals the diffuse transmission characteristics of visible/NIR in fruit tissue, which can provide a realistic basis for developing online sorting devices for the quality of thick-skinned fruits.