Journal Highlight: Territorial origin of olive oil: representing georeferenced maps of olive oils by NMR profiling

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  • Published: Jul 3, 2017
  • Author: spectroscopyNOW
  • Channels: NMR Knowledge Base
thumbnail image: Journal Highlight: Territorial origin of olive oil: representing georeferenced maps of olive oils by NMR profiling

1H NMR spectra have been used to build a geographic information system map permitting the identification of territorial regions of Italian olive oils having strong similarities in chemical content.

Territorial origin of olive oil: representing georeferenced maps of olive oils by NMR profiling

Magnetic Resonance in Chemistry, 2017, 55, 639-647
Raffaele Lamanna, Giovanna Imparato, Paola Tano, Angela Braca, Mario D'Ercole and Giovanni Ghianni

Abstract: Proton NMR profiling is nowadays a consolidated technique for the identification of geographical origin of food samples. The common approach consists in correlating NMR spectra of food samples to their territorial origin by multivariate classification statistical algorithms. In the present work, we illustrate an alternative perspective to exploit territorial information, contained in the NMR spectra, which is based on the implementation of a geographic information system (GIS). Nuclear magnetic resonance spectra are used to build a GIS map permitting the identification of territorial regions having strong similarities in the chemical content of the produced food (terroir units). These terroir units can, in turn, be used as input for labeling samples to be analyzed by traditional classification methods. In this work, we describe the methods and the algorithms that permit to produce GIS maps from NMR profiles and apply the described method to the analysis of the geographical distribution of olive oils in an Italian region. In particular, we analyzed by 1H NMR up to 98 georeferenced olive oil samples produced in the Abruzzo Italian region. By using the first principal component of the NMR variables selected according to the Moran test, we produced a GIS map, in which we identified two regions incidentally corresponding to the provinces of Teramo and Pescara. We then labeled the samples according to the province of provenience and built an LDA model that provides a classification ability up to 99% . A comparison between the variables selected in the geostatistics and classification steps is finally performed.

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