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Multiple Assignment Recovered Analysis (MARA) NMR for a Direct Food Labeling: the Case Study of Olive Oils

  • Archimede RotondoEmail author
  • Luisa Mannina
  • Andrea Salvo
Article

Abstract

The multiple assignment recovered analysis (MARA) on nuclear magnetic resonance (NMR) spectra is here presented with the aim to provide the quantitative label of chemical mixtures such as foodstuff. The method takes advantage from the multiple NMR signals generated by any chemical; these will be all proportional to the concentration of the parent compound. In a well-known system, the selection of many integration regions enables the development of simple relationships fulfilled just by specific quantitative values of the expected components. As long as the number of equations is bigger than the known quantitative variables, MARA-NMR best-fitting algorithm will be suitably designed to output trustworthy and robust results. This is definitely demonstrated for the extra-virgin olive oil (EVOO) taken as case study: MARA-NMR showed consistency with traditional analytical measurements over 30 specimens recording satisfactory repeatability. The minimization procedure is applied by tuning the quantitative variables; these are affecting the function ρ which represents the experimental lapse from the corresponding theoretical dataset. MARA-NMR is an effective, innovative, and quick method for food labeling; unlike other analytical techniques, it is self-consistent smoothing out random instrumental outliers or unpredictable anomalies. MARA is customized in order to be versatile paving the way for new updated, extended, and refined labeling protocols and also for the extension of this approach on the study of whichever matrix.

Keywords

Multiple integration Quantitative analysis NMR Food chemical label Olive oil 

Notes

Compliance and Ethical Standards

Conflict of Interest

Archimede Rotondo declares that he has no conflict of interest; Luisa Mannina declares that she has no conflict of interest; Andrea Salvo declares that he has no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

Not applicable.

Supplementary material

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12161_2019_1460_MOESM2_ESM.xlsm (45 kb)
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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Biomedical and Dental Sciences and Morphofunctional ImagingUniversity of MessinaMessinaItaly
  2. 2.Department of Chemistry and Drug TechnologyUniversity of Roma “La Sapienza”RomeItaly

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