The assignment of significantly changed NMR signals, which were identified with the help of multivariate models, to individual metabolites in biofluids is a manual and tedious task requiring knowledge in chemometrics and NMR spectroscopy. Metabolite projection analysis, introduced in this work, allows automatic linking of multivariate models with metabolites by skipping the level of manual NMR signal identification. The method depends on the projection of sets of metabolite NMR spectra from a database into PCA or PLS models of NMR spectra of biofluid samples. Metabolites that are significantly changed can be identified graphically in metabolite projection plots or numerically as projected virtual concentration. The method is demonstrated together with a newly introduced algorithm for refined nonequidistant binning using a metabonomics study with amiodarone as administered drug. Amiodarone can induce phospholipidosis in the lung and liver, which is accompanied by associated organ toxicity in these organs. It is shown how metabolite projection analysis allows easy and fast tentative assignment of all structures of metabolites whose concentrations in the urine samples significantly changed upon dosage. These metabolites had also been identified previously by manually interpreting the multivariate models and spectra. Among these metabolites, phenylacetylglycine was also identified as being significantly increased. This metabolite has recently been proposed as urinary biomarker for phospholipidosis.