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In the process of acquiring the visible/near infrared (Vis/NIR) spectra of mandarin to predict the soluble solids content (SSC), spectra information is often influenced by the physical property of citrus. Thereupon affects the accuracy and robustness of the SSC prediction model. Calibration models were established by partial least squares (PLS) regression methods based on three spectral measurement orientations (Mandarin calyx-stem axis vertical with stem upward (A); Mandarin calyx-stem axis vertical with stem downward (B); Mandarin calyx-stem axis horizontal (C)) respectively. Novel methods that considering mandarin external factors (diameter, color and peel thickness) were established. The methods were associated with wavelength selection algorithms and their combination with alternative methods to build multivariate regression models. After evaluating the models, the model performance was improved by the external factor inclusion methods, among which the color and diameter united inclusion method combined with ACO-PLS algorithm performed the best, with Rp = 0.987 and RMSEP = 0.19 degrees Brix for its prediction model. This study serves as a reference and theoretical support for determining the influencing factors that affect fruit internal quality determination using near infrared spectroscopy.