Unravelling proton NMR spectra of foodstuffs with independent component analysis techniques

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  • Published: Mar 14, 2014
  • Author: Steve Down
  • Channels: UV/Vis Spectroscopy / Raman / X-ray Spectrometry / Infrared Spectroscopy / MRI Spectroscopy / Proteomics / NMR Knowledge Base / Chemometrics & Informatics / Base Peak / Atomic

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Overlapping signals in the proton NMR spectra of complex liquids, like foodstuffs and soft drinks, can be processed using a chemometric method, independent component analysis, to extract the spectra of pure components and determine their concentrations. Scientists from Russia and Germany showed how a number of ICA procedures could deconvolute a series of model mixtures before applying them to real samples, as described in Magnetic Resonance in Chemistry.

The ICA methods 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) were compared with the deconvolution methods simple-to-use-interactive self-modeling mixture analysis (SIMPLISMA) and multivariate curve resolution-alternating least squares (MCR-ALS). In general, they all processed the spectra of binary liquids satisfactorily with SIMPLISMA and JADE performing the best but for multicomponent mixtures, SIMPLISMA and MILCA were best for quantitative analysis.

Using up to eight components of honey in various mixtures, the maximum number of components that can be analysed was found to be eight but this would suffice for analysing the major components in many foodstuffs. The power of ICA was illustrated by measuring the content of glucose, fructose and sucrose in cola, with MILCA the most accurate technique. The levels of 1,2-propanediol, ethylene glycol, glycerol and 1,3-propanediol in the liquids for e-cigarettes were also successfully measured, agreeing with those determined by GC/MS.

This is the first time that ICA has been applied to NMR spectra, having been used successfully in the past to process IR and fluorescence data. It does not require prior knowledge of the number of components in the system and will improve the analysis of foodstuffs. The research team recommend that NMR instrument manufacturers incorporate ICA into the data processing software.


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