To discriminate orange juice from grapefruit juice in a context of fraud prevention, (1)H NMR data were submitted to different treatments to extract informative variables which were then analysed using multivariate techniques. Averaging contiguous data points of the spectrum followed by logarithmic transformation improved the results of the data analysis. Moreover, supervised variable selection methods gave better rates of classification of the juices into the correct groups. Last, independent-component analysis gave better classification results than principal-component analysis. Hence, ICA may be an efficient chemometric tool to detect differences in the (1)H NMR spectra of similar samples, and so may be useful for authentication of foods.