The possibility of using near-infrared spectroscopy (NIRS) for the authentication of wild European sea bass ( Dicentrarchus labrax ) was investigated in this study. Three different chemometric techniques to process the NIR spectra were developed, and their ability to discriminate between wild and farmed sea bass samples was evaluated. One approach used spectral information to directly build the discrimination model in a latent variable space; the second approach first used wavelets to transform the spectral information and subsequently derived the discrimination model using the transformed spectra; in the third approach a cascaded arrangement was proposed whereby very limited chemical information was first estimated from spectra using a regression model, and this estimated information was then used to build the discrimination model in a latent variable space. All techniques showed that NIRS can be used to reliably discriminate between wild and farmed sea bass, achieving the same classification performance as classification methods that use chemical properties and morphometric traits. However, compared to methods based on chemical analysis, NIRS-based classification methods do not require reagents and are simpler, faster, more economical, and environmentally safer. All proposed techniques indicated that the most predictive spectral regions were those related to the absorbance of groups CH, CH(2), CH(3), and H(2)O, which are related to fat, fatty acids, and water content.