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A Two-Scale Pursuit Method for the Tailored Identification and Quantification of Unknown Polymer Additives and Contaminants by 1H NMR
journal contribution
posted on 2015-03-18, 00:00 authored by Phuong-Mai Nguyen, Cédric Lyathaud, Olivier VitracBlind 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 1H 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.
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Keywords
undocumented onestolerance ranges52 substancesspectroscopic techniquesplastic materialspreference listTailored Identification1 H NMR spectrumMany substancesNMR spectraUnknown Polymer Additivespeak linesfood packaging systemsdeconvolution procedurecrude pairwise searchIdentified substances279 multipletsvoting system1 H NMRBlind deformulationGaussian kernels
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