Data fusion between high resolution H-1-NMR and mass spectrometry: a synergetic approach to honey botanical origin characterization
2016
Spiteri, Marc | Dubin, Elodie | Cotton, Jérôme | Poirel, Marion | Corman, Bruno | Jamin, Eric | Lees, Michele | Rutledge, Douglas | Eurofins Analytics France | Profilomic [Boulogne-Billancourt] | Profilomic | Ingénierie, Procédés, Aliments (GENIAL) ; Institut National de la Recherche Agronomique (INRA)-AgroParisTech | This work was funded by Bpifrance as part of the AgriFood GPS (Global Protection System) collaborative project (grant no. I1110023W). Elodie Dubin, Marc Spiteri and Jerome Cotton are supported by CIFRE grants (no. 2012/0874, 2011/1506 and 2011/1064) from Association Nationale de Recherche et de Technologie
A data fusion approach was applied to a commercial honey data set analysed by H-1-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.
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