DISCRIMINATION OF ARABICA AND ROBUSTA IN INSTANT COFFEE BY FOURIER-TRANSFORM INFRARED-SPECTROSCOPY AND CHEMOMETRICS

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