Two species of coffee bean have acquired worldwide economic importance: these are, Coffea Arabica and Coffea Canephora variant Robusta. Arabica beans are valued most highly by the trade, as they are considered to have a finer flavor than Robusta. In this work, Fourier transform infrared spectroscopy is explored as a rapid alternative to wet chemical methods for authentication and quantification of coffee products. Principal component analysis (PCA) is applied to spectra of freeze-dried instant coffees, acquired by DRIFT (diffuse reflection infrared Fourier transform) and ATR (attenuated total reflection) sampling techniques, and reveals clustering according to coffee species. Linear discriminant analysis of the principal component scores yields 100% correct classifications for both training and test samples. The chemical origin of the discrimination is explored through interpretation of the PCA loadings. Partial least squares regression is applied to spectra of Arabica and Robusta blends to determine the relative content of each species. Internal cross-validation gives a correlation coefficient of 0.99 and a standard error of prediction of 1.20% (w/w), illustrating the potential of the method for industrial off-line quality control analysis.