A Two-Scale Pursuit Method for the Tailored Identification and Quantification of Unknown Polymer Additives and Contaminants by ¹H NMR

Abstract

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 1 H 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.

Library-assisted nonlinear blind separation and annotation of pure components from a single 1H nuclear magnetic resonance mixture spectra

“…Metabolic profiling aims to identify and quantify small-molecule analytes (a.k.a. metabolites or pure components) present in complex multicomponent mixtures acquired in drug development [1,2], toxicology studies [3], disease diagnosis [4,5], food, nutrition and environmental sciences [6][7][8]. Because both techniques provide structural information on chemical classes in a single analysis, metabolic profiling technologies are mainly based on nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry.…”

“…Candidates for biomarkers are obtained through matching acquired spectra with those from a library [9], such as the BioMagResBank metabolomics database [10] or Wiley 1 H NMR database [11]. However, because many metabolites are structurally similar, their NMR spectra are highly correlated, with many overlapping peaks [12,8]. That makes metabolic profiling a notoriously difficult problem.…”

“…That is especially true for 1 H NMR spectroscopy [9], which, due to its capability for high-throughput screening [13], is routinely used for metabolite biomarker research. Difficulties related to ambiguous elucidation of the chemical structures are caused by 1 H-1 H J-couplings that generate broad multiplets [14,8]. Since many metabolites are not species dependent, that allows translation of some specific biomarkers from preclinical studies directly in clinical studies [15].…”

“…Therefore, for the present study an in-house library comprised of 160 1 H NMR spectra of pure components is built, whereas many of them are known to be present in urine samples of diabetic patients. 1 The above discussion suggests that computational methods for multivariate analysis of complex metabolomic datasets are of utmost importance for extraction of metabolic information, sample classification and biomarker discovery, [17,18,12,8]. Thus, the main motivation of this paper is: development of a method for blind separation of nonnegative correlated sources from single nonlinear mixture.…”