The quality of wheat flour (WF) is among the highest of cereal flours and therefore, it is one of the most expensive flours for manufacturing food products. In developing countries, adulteration of WF by mixing up with lower price cereal flours is often seen. Hence, the classification and determination of the adulteration quantity in WF is of great interest. The aim of this research was to evaluate the feasibility of FT-IR spectroscopy and multivariate data analysis methods for the detection of adulteration of WF with the most likely adulterant barley flour (BF). For this purpose, 20 pure cereal flours and 120 flour blends were analyzed using FT-IR spectroscopy with chemometrics. The spectra were collected in the region of 4000–450 cm−1 and up to 15 wavenumber regions corresponding to peaks of flour constituents were selected. The classification limit value of soft independent modeling of class analogies for detection of BF added to WF was better than 1%. Additionally, 98.25% of the flours were correctly classified by linear discriminant analysis. Partial least squares regression was adopted to construct a model to quantify the adulteration level. The root mean square error of calibration for sample calibration associated forecast parameters was 0.34–1.34% and the root mean square error of cross validation was 0.36–1.50%. Thus, the BF adulterant could be detected down to approximately 0.30%.