Blind deformulation is an important stake for several industries. This work was motivated by the identification and quantification of contaminants originated from food packaging systems. Many substances originating from plastic materials are indeed suspected to be endocrine disruptors but remain chiefly difficult to separate with spectroscopic techniques. We propose a tailored two-scale pursuit methodology to identify and quantify an arbitrary number of substances from the H-1 NMR spectrum of the mixture. Identified substances are included within a library of spectra and can be combined with undocumented ones. To preserve the initial resolution of NMR spectra, peak lines are spanned onto Gaussian kernels so that they can be identified, even when the positions and shapes of multiplets in the mixture are modified within tolerance ranges or when multiplets are overlapping. The deconvolution procedure starts with a crude pairwise search to build a list of likely substances, which is subsequently expanded as nested scenarios. Scenarios are built according to the risk of confusing similar substances. Quantification is carried out on a preference list of substances selected as in a voting system. Using a primary library of 52 substances (corresponding to 279 multiplets and 5620 lines), the reliability and robustness of the method were tested extensively in numerical experiments and by performing the brute-force deformulation of five processed common thermoplastics.