Data fusion between high resolution 1H-NMR and mass spectrometry: a synergetic approach to honey botanical origin characterization

AbstractA data fusion approach was applied to a commercial honey data set analysed by 1H-nuclear magnetic resonance (NMR) 400 MHz and liquid chromatography-high resolution mass spectrometry (LC-HRMS). The latter was performed using two types of mass spectrometers: an Orbitrap-MS and a time of flight (TOF)-MS. Fifty-six honey samples from four monofloral origins (acacia, orange blossom, lavender and eucalyptus) and multifloral sources from various geographical origins were analysed using the three instruments. The discriminating power of the results was examined by PCA first considering each technique separately, and then combining NMR and LC-HRMS together with or without variable selection. It was shown that the discriminating potential is increased through the data fusion, allowing for a better separation of eucalyptus, orange blossom and lavender. The NMR-Orbitrap-MS and NMR-TOF-MS mid-level fusion models with variable selection were preferred as a good discrimination was obtained with no misclassification observed for the latter. This study opens the path to new comprehensive food profiling approaches combining more than one technique in order to benefit from the advantages of several technologies. Graphical AbstractData fusion between high resolution 1H-NMR and mass spectrometry

[1]  Benachir Bouchikhi,et al.  Classification of Honey According to Geographical and Botanical Origins and Detection of Its Adulteration Using Voltammetric Electronic Tongue , 2016, Food Analytical Methods.

[2]  Joshua D. Knowles,et al.  Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry , 2011, Nature Protocols.

[3]  C T Elliott,et al.  Discrimination of honey of different floral origins by a combination of various chemical parameters. , 2015, Food Chemistry.

[4]  Steffen Neumann,et al.  Highly sensitive feature detection for high resolution LC/MS , 2008, BMC Bioinformatics.

[5]  D. Lachenmeier,et al.  Qualitative and Quantitative Control of Honeys Using NMR Spectroscopy and Chemometrics , 2013 .

[6]  Dan Ventura,et al.  LC-MS alignment in theory and practice: a comprehensive algorithmic review , 2013, Briefings Bioinform..

[7]  Steven J Lehotay,et al.  Determination of pesticide residues in foods by acetonitrile extraction and partitioning with magnesium sulfate: collaborative study. , 2007, Journal of AOAC International.

[8]  M. Fielder,et al.  The application of high resolution diffusion NMR to the analysis of manuka honey. , 2012, Food chemistry.

[9]  M. Cabezudo,et al.  Free amino acid composition and botanical origin of honey , 2003 .

[10]  S. Mammi,et al.  Characterization of markers of botanical origin and other compounds extracted from unifloral honeys. , 2013, Journal of agricultural and food chemistry.

[11]  Andrea D. Magrì,et al.  Data-fusion for multiplatform characterization of an Italian craft beer aimed at its authentication. , 2014, Analytica chimica acta.

[12]  F Savorani,et al.  icoshift: A versatile tool for the rapid alignment of 1D NMR spectra. , 2010, Journal of magnetic resonance.

[13]  Robert S Plumb,et al.  Statistical heterospectroscopy, an approach to the integrated analysis of NMR and UPLC-MS data sets: application in metabonomic toxicology studies. , 2006, Analytical chemistry.

[14]  Frans M van der Kloet,et al.  Analytical error reduction using single point calibration for accurate and precise metabolomic phenotyping. , 2009, Journal of proteome research.

[15]  Manfred Spraul,et al.  Synergistic effect of the simultaneous chemometric analysis of ¹H NMR spectroscopic and stable isotope (SNIF-NMR, ¹⁸O, ¹³C) data: application to wine analysis. , 2014, Analytica chimica acta.

[16]  L. Svečnjak,et al.  Optimization of FTIR-ATR spectroscopy for botanical authentication of unifloral honey types and melissopalynological data prediction , 2015, European Food Research and Technology.

[17]  Christophe Junot,et al.  High-resolution mass spectrometry associated with data mining tools for the detection of pollutants and chemical characterization of honey samples. , 2014, Journal of agricultural and food chemistry.

[18]  Ionara R Pizzutti,et al.  Method validation and comparison of acetonitrile and acetone extraction for the analysis of 169 pesticides in soya grain by liquid chromatography-tandem mass spectrometry. , 2009, Journal of chromatography. A.

[19]  Anne-Béatrice Dufour,et al.  The ade4 Package: Implementing the Duality Diagram for Ecologists , 2007 .

[20]  R. Abagyan,et al.  XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. , 2006, Analytical chemistry.

[21]  J. Riedl,et al.  Review of validation and reporting of non-targeted fingerprinting approaches for food authentication. , 2015, Analytica chimica acta.

[22]  David Broadhurst,et al.  The importance of experimental design and QC samples in large-scale and MS-driven untargeted metabolomic studies of humans. , 2012, Bioanalysis.

[23]  D. Rutledge,et al.  Common components and specific weights analysis: a tool for metabolomic data pre-processing , 2016 .

[24]  R. Consonni,et al.  NMR characterization of saccharides in Italian honeys of different floral sources. , 2012, Journal of agricultural and food chemistry.

[25]  C. Guyot-Declerck,et al.  Floral quality and discrimination of Lavandula stoechas, Lavandula angustifolia, and Lavandula angustifolia×latifolia honeys , 2002 .

[26]  Douglas N Rutledge,et al.  Fast and global authenticity screening of honey using ¹H-NMR profiling. , 2015, Food chemistry.

[27]  Robert Burke,et al.  ProteoWizard: open source software for rapid proteomics tools development , 2008, Bioinform..