Independent component analysis (ICA) algorithms for improved spectral deconvolution of overlapped signals in 1H NMR analysis application to foods and related products

Independent component analysis (ICA) algorithms for improved spectral deconvolution of overlapped signals in 1H NMR analysis : application to foods and related products

Copyright © 2014 John Wiley & Sons, Ltd.

Bibliographische Detailangaben
Veröffentlicht in:Magnetic resonance in chemistry : MRC. - 1985. - 52(2014), 5 vom: 24. Mai, Seite 231-40
1. Verfasser: Monakhova, Yulia B (VerfasserIn)
Weitere Verfasser: Tsikin, Alexey M, Kuballa, Thomas, Lachenmeier, Dirk W, Mushtakova, Svetlana P
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2014
Zugriff auf das übergeordnete Werk:Magnetic resonance in chemistry : MRC
Schlagworte:Journal Article 1H NMR e-cigarettes food products independent component analysis multivariate curve resolution non-alcoholic beverages Protons
Beschreibung
Zusammenfassung:Copyright © 2014 John Wiley & Sons, Ltd.
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
Beschreibung:Date Completed 24.11.2014
Date Revised 02.12.2018
published: Print-Electronic
Citation Status MEDLINE
ISSN:1097-458X
DOI:10.1002/mrc.4059