Visible/near infrared spectroscopy combined with chemometrics methods was used to detect ternary system adulteration in camellia oil quantificationally. In order to get adulterated samples, rapeseed oil and peanut oil were added to pure camellia oil in different proportion. Visible/near infrared spectroscopy data of pure and adulterated camellia oil samples were acquired in the wavelength range of 350~1800nm, and samples were randomly divided into calibration set and prediction set. The adulteration models were optimized by comparing different wavelength ranges, pretreatment methods and calibration methods The results show that the optimal modeling wavelength ranges and pretreatment methods for the prediction models of rapeseed oil, peanut oil and total adulteration amount are 750~1 770, 900~1 770, 870~1 770 nm and Multiple scattering correction (MSC), Standard normal variate (SNV) and second order differentia, and the best modeling method is Least square support vector machine (LSSVM). The correlation coefficient (RP) in prediction set and the root mean square error predictions(RMSEPs) of optimal adulteration models for rapeseed oil, peanut oil and total adulteration are 0.963, 0.982, 0.993 and 2.1%, 1.5%, 1.8%, respectively. Thus it can be seen that visible /near infrared spectroscopy combined with chemometrics methods can be used for quantitative ternary system adulteration detection in camellia oil.