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An evaluation has been made of the potential of near-infrared (NIR) technologies in the assessment of essential oil components and in the identification of individual essential oils. The results showed that cross-validation models are able to predict accurately almost all of the components of essential oils. In different cinnamon (Cinnamomum zeylanicum) and clove (Syzygium aromaticum) essential oils, which showed a similar composition, 23 components (representing 97.8-99.9% of the oil) were accurately predicted, as well as 20 components (93.0-99.1%) in Cinnamomum camphora (ravintsara), 32 components (92.3-98.1%) in Ravensara aromatica (ravensara), and 26 components (96.6-98.4%) in Lippia multiflora. For almost all of the components, the modelled and reference values obtained by GC-FID were highly correlated (r2 > or = 0.985) and exhibited a low variance (less than 5%). The model was also able to discriminate between the ravintsara and ravensara essential oils. It was shown that two commercial oils labelled as R. aromatica were actually ravintsara (C. camphora), revealing the misidentification of these essential oils in the marketplace. The study demonstrates the application of NIR technology as a quality control tool for the rapid identification of individual essential oils, for product authentication, and for the detection of adulteration.