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Independent component analysis (ICA) algorithms for improved spectral deconvolution of overlapped signals in 1 H NMR analysis: application to foods and related products
Author(s) -
Monakhova Yulia B.,
Tsikin Alexey M.,
Kuballa Thomas,
Lachenmeier Dirk W.,
Mushtakova Svetlana P.
Publication year - 2014
Publication title -
magnetic resonance in chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.483
H-Index - 72
eISSN - 1097-458X
pISSN - 0749-1581
DOI - 10.1002/mrc.4059
Subject(s) - independent component analysis , deconvolution , chemistry , principal component analysis , biological system , chemometrics , component (thermodynamics) , resolution (logic) , analytical chemistry (journal) , component analysis , algorithm , nmr spectra database , pattern recognition (psychology) , spectral line , chromatography , artificial intelligence , computer science , physics , astronomy , biology , thermodynamics
The major challenge facing NMR spectroscopic mixture analysis is the overlapping of signals and the arising impossibility to easily recover the structures for identification of the individual components and to integrate separated signals for quantification. In this paper, various independent component analysis (ICA) algorithms [mutual information least dependent component analysis (MILCA); stochastic non‐negative ICA (SNICA); joint approximate diagonalization of eigenmatrices (JADE); and robust, accurate, direct ICA algorithm (RADICAL)] as well as deconvolution methods [simple‐to‐use‐interactive self‐modeling mixture analysis (SIMPLISMA) and multivariate curve resolution‐alternating least squares (MCR‐ALS)] are applied for simultaneous 1 H NMR spectroscopic determination of organic substances in complex mixtures. Among others, we studied constituents of the following matrices: honey, soft drinks, and liquids used in electronic cigarettes. Good quality spectral resolution of up to eight‐component mixtures was achieved (correlation coefficients between resolved and experimental spectra were not less than 0.90). In general, the relative errors in the recovered concentrations were below 12%. SIMPLISMA and MILCA algorithms were found to be preferable for NMR spectra deconvolution and showed similar performance. The proposed method was used for analysis of authentic samples. The resolved ICA concentrations match well with the results of reference gas chromatography–mass spectrometry as well as the MCR‐ALS algorithm used for comparison. ICA deconvolution considerably improves the application range of direct NMR spectroscopy for analysis of complex mixtures. Copyright © 2014 John Wiley & Sons, Ltd.