Puffy fruit and soluble solid content (SSC) are important indexes for evaluating the quality of citrus. The feasibility was discussed for detecting Puffing disease and SSC of intact citrus simultaneously by online visible-near infrared (visible-NIR) transmittance spectroscopy. The spectra were recorded with the integration time of 100 ms in the wavelength range of 350~1 150 nm when the samples were conveyed at the speed of five samples per second. The feasibility of simultaneous and online detection of puffiness fruit and SSC for intact citrus simultaneously was discussed by visible-near infrared transmittance spectroscopy. The response properties of visible-NIR spectra for normal fruit, mild and severe puffiness fruit were analyzed. Then least squares support vector machine (LSSVM) and discrimination partial least square (DPLS) were developed for discrimination of puffiness fruit and health citrus. At the same time, the optimal soluble solids content model of citrus was conducted by partial least squares regression methods. Other 35 samples without developing calibration models were applied to evaluate precision of online sorting. The classification rate was 100% for identifying puffiness fruit, and the accuracy of sorting SSC for health pears was 97%. The results showed that simultaneous detection of puffiness and SSC were feasible by visible-NIR transmittance spectroscopy.