In this study, Fourier transform infrared (FTIR) spectroscopy in tandem with chemometrics was used for the discrimination and quantification of the adulterants, such as benzyl alcohol (B) and ethyl acetate (E) in clove essential oil (CO). Different multivariate models with various spectral derivatization methods were developed and their analysis abilities the adulterants were compared using statistical quality parameters. To discriminate the adulterations thanks to the FTIR data, 130 chemometric models were built by principal component analysis (PCA) algorithm. The statistical performances of the PCA models developed were evaluated by the number of samples outside of explained variance (95 %) and eigen value. To quantify the adulterant concentrations in CO samples, 117 partial least squares (PLS) regression models employing the FTIR data were developed. To find out the best PLS model, the root mean square error of prediction (RMSEP), and root mean square error of cross-validation (RMSECV) were mainly used. Root mean square error of calibration (RMSEC) and R-square were also evaluated. The best discrimination results were achieved using 1st derivative spectra in the region 3500-3100 cm−1 (SRB1) and 2nd derivative spectra in the region 1077-1008 cm-1 (SRE3) for the adulterants of B and E, respectively. The best PLS calibration results were obtained from the combinations of the normal spectra in the regions 3500-3100 cm−1, 1027-993 cm−1, and 756-569 cm−1 (SRBC) and SRE3 for the quantification of B and E, respectively. The results of the study indicated that FTIR with chemometrics could be used for simultaneously discrimination and quantification of the adulterants of B and E in COs without using any toxic chemicals or pretreatments.