In this study, a quick and simple method was developed for detection of tahini adulteration with sunflower oil. The synchronous fluorescence spectroscopy (SFS) data of oil samples were collected by scanning the excitation and emission monochromators simultaneously with 20, 40, 60 and 80 nm wavelength intervals within the range of 250-600 nm. Three different multivariate calibration methods, namely partial least squares (PLS) analysis, principal component regression (PCR), and multiple linear regression (MLR) were used for data analysis. Wavelength selection feature of the chemometric software was also used in order to determine the optimum range of each dataset collected at 20, 40, 60 and 80 nm wavelength intervals. All regression methods with and without wavelength selection mode were applied to these each dataset individually. Application of wavelength selection mode adversely affected the root mean square error of cross validation (RMSECV) and root mean square error of prediction (RMSEP) values and other quality parameters of all calibration and validation models which were built by using each dataset collected at 20, 40, 60 and 80 nm wavelength intervals. Taking all parameters into consideration, the best results were obtained through the application of PLS analysis without wavelength selection mode on the SFS data collected at all wavelength intervals. The lowest detection limits of adulteration, 0.09% and 0.15% were obtained through the use of 40 and 80 nm as wavelength intervals, respectively. RMSECV and RMSEP values were calculated as 0.74 and 1.26 for 40 nm, and 0.65 and 0.81 for 80 nm wavelength intervals.