The FT-NIR transmission spectra of ternary blended edible oil samples were collected over 10 000-4 200 cm-1. After being pretreated with different methods, the calibration models of quantitative analysis of soybean oil, peanut oil and corn oil contents in ternary blended edible oil were established using partial least square (PLS) regression. The accuracy and precision of the models for the predicted sample set were examined to make sure of the practicability of the models. After being pretreated with first derivative and multiplicative signal correction (FD+MSC), the optimal soybean oil NIR model was built over 5 450.1-4 597.7 cm-1. The best prediction model for peanut oil was established between 7 521.3 and 6 098.1 cm-1 after using first derivative with straight line subtraction (FD+SLS) preprocess method. The best pretreated method and the best spectrum range for corn oil content model were first derivative (FD) and 9 993.7-7 498.2 cm-1, respectively. The best correlation coefficients (R2) of the three prediction models were 99.89%, 99.88% and 99.76%, respectively. The RMSEP of the soybean oil content model was 1.09%, while the peanut oil prediction model’s RMSEP was 1.17%, and 1.48% for the corn oil prediction model. The values of the t-test were between 0.007 9 and 0.371 9, and all values of the relative standard deviation (RSD) were less than 1.50%. The results showed that NIR could be an ideal tool for fast determination of the soybean oil, peanut oil and corn oil contents in ternary blended edible oil.