For the first time, a quick and simple analytical method was developed for the discrimination and the quantification of low level (< 10%) of safflower oil (SAF) in sunflower oil (SUN). Different spectroscopic techniques, such as attenuated total reflectance-Fourier transform infrared (ATR-FTIR), ultraviolet-visible (UV-Vis), and fluorescence (FL), have been used and their abilities to quantify SAF content in SUN were compared using statistical quality parameters of the developed calibration models. To quantify SAF content in SUN samples, partial least squares (PLS) regression (model 1) was used. To correlate SAF in the calibration set with their spectra, 30 multivariate calibration models were built by PLS algorithm. The performance and accuracy of PLS models were evaluated by the value of R-square, root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), and root mean square error of prediction (RMSEP). To detect SAF adulterations, several principal component analysis (PCA) models (model 2) were utilized. The performance of PCA models developed was evaluated by principal components (PC) percentages, the factor numbers reached, F-Residuals limits, and Hotelling’s T2 limits. Both for the quantify and the discrimination, the best predictions were achieved using normal spectra in FL spectroscopy with the lowest RMSEC of 0.2553, RMSEV of 0.5398, RMSEP of 0.2674, and F-Residuals limit of 0.7040 and with the highest R-square of 0.9937, PC percentages of 69%, the factor numbers of 7, and Hotelling’s T2 limit of 13.3208 in all spectral region.