The use of Fourier transform infrared (FTIR) spectroscopy coupled with chemometric techniques to differentiate butter from beef fat (BF) was investigated. The spectral bands associated with butter, BF, and their mixtures were scanned, interpreted, and identified by relating them to those spectroscopically representative to pure butter and BF. For quantitative analysis, partial least square (PLS) regression was used to develop a calibration model at the selected fingerprint regions of 1500-1000 cm-1, with the values of coefficient of determination (R2) and root mean square error of calibration (RMSEC) are 0.999 and 0.89% (v/v), respectively. The PLS calibration model was subsequently used for the prediction of independent samples containing butter in the binary mixtures with BF. Using 6 principal components, root mean square error of prediction (RMSEP) is 2.42% (v/v). These results proved that FTIR spectroscopy in combination with multivariate calibration can be used for the detection and quantification of BF in butter formulation for authentication use.