One of the major challenges in NMR/mass spectrometry analysis of metabolic profiles relies onthe identification and quantification of metabolites in complex biological mixtures. These features aremandatory to make metabolomics asserting a general approach to test a priori formulated hypotheseson the basis of exhaustive metabolome characterization rather than an exploratory tool dealing with un-known metabolic features. In this communication we propose a method, named ASICS, based on astrong statistical theory that handles automatically the metabolites identification and quantification inproton NMR spectra. A statistical linear model is built to explain a complex spectrum using a librarycontaining pure metabolite spectra. This model can handle local or global chemical shift variations dueto experimental conditions using a warping function. A statistical lasso-type estimator identifies andquantifies the metabolites in the complex spectrum. This estimator shows good statistical properties andhandles peak overlapping issues. The performances of the method were investigated on known mixtures(such as synthetic urine) and on plasma datasets from duck and human (plasma NIST SRM1950) using alibrary of 175 pure metabolites. Results show noteworthy performances, outperforming current existingmethods (namely MetaboHunter, BATMAN, Bayesil or Chenomx) for the identification and the quan-tification of metabolites. Furthermore, ASICS could easily be used routinely according to its reasonablecomputational time (no more than 3 minutes for the whole analysis of a complex spectrum). In conclu-sion, ASICS is a completely automated procedure for metabolites identification and quantification in 1HNMR spectra of biological mixtures. It will enable empowering NMR-based metabolomics by quicklyand accurately helping experts to obtain metabolic profiles. ASICS is already available as a R function orwithin the more user-friendly Galaxy/W4M interface developed by the MetaboHUB infrastructure andIFB (French Institute of Bioinformatics).