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Analytical and Bioanalytical Chemistry

, Volume 409, Issue 1, pp 307–315 | Cite as

A strategy for simultaneous determination of fatty acid composition, fatty acid position, and position-specific isotope contents in triacylglycerol matrices by 13C-NMR

  • Noelle Merchak
  • Virginie Silvestre
  • Denis Loquet
  • Toufic Rizk
  • Serge Akoka
  • Joseph BejjaniEmail author
Research Paper

Abstract

Triacylglycerols, which are quasi-universal components of food matrices, consist of complex mixtures of molecules. Their site-specific 13C content, their fatty acid profile, and their position on the glycerol moiety may significantly vary with the geographical, botanical, or animal origin of the sample. Such variables are valuable tracers for food authentication issues. The main objective of this work was to develop a new method based on a rapid and precise 13C-NMR spectroscopy (using a polarization transfer technique) coupled with multivariate linear regression analyses in order to quantify the whole set of individual fatty acids within triacylglycerols. In this respect, olive oil samples were analyzed by means of both adiabatic 13C-INEPT sequence and gas chromatography (GC). For each fatty acid within the studied matrix and for squalene as well, a multivariate prediction model was constructed using the deconvoluted peak areas of 13C-INEPT spectra as predictors, and the data obtained by GC as response variables. This 13C-NMR-based strategy, tested on olive oil, could serve as an alternative to the gas chromatographic quantification of individual fatty acids in other matrices, while providing additional compositional and isotopic information.

Graphical abstract

A strategy based on the multivariate linear regression of variables obtained by a rapid 13C-NMR technique was developed for the quantification of individual fatty acids within triacylglycerol matrices. The conceived strategy was tested on olive oil.

Keywords

INEPT Multivariate linear regression Triacylglycerol Olive oil Fatty acids Squalene 

Notes

Acknowledgments

N.M. acknowledges the financial support of the Lebanese National Council for Scientific Research and the Research Council of Saint-Joseph University. The CORSAIRE platform from Biogenouest is also acknowledged. The authors acknowledge Anne Ancelin for linguistic assistance.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

216_2016_5_MOESM1_ESM.pdf (268 kb)
ESM 1 (PDF 267 kb)

References

  1. 1.
    Wishart DS. Quantitative metabolomics using NMR. Trends Anal Chem. 2008;27:228–37.CrossRefGoogle Scholar
  2. 2.
    Wollenberg KF. Quantitative high resolution 13C nuclear magnetic resonance of the olefinic and carbonyl carbons of edible vegetable oils. J Am Oil Chem Soc. 1990;67:487–94.CrossRefGoogle Scholar
  3. 3.
    Sacchi R, Addeo F, Paolillo L. 1H and 13C NMR of virgin olive oil. An overview. Magn Reson Chem. 1997;35:S133–45.CrossRefGoogle Scholar
  4. 4.
    Vlahova G. Application of NMR to the study of olive oils. Prog Nucl Magn Reson Spectrosc. 1999;35:341–57.CrossRefGoogle Scholar
  5. 5.
    Vlahova G, Shawb AD, Kell DB. Use of 13C nuclear magnetic resonance distortionless enhancement by polarization transfer pulse sequence and multivariate analysis to discriminate olive oil cultivars. J Am Oil Chem Soc. 1999;76:1223–31.CrossRefGoogle Scholar
  6. 6.
    Zamora R, Alba V, Hidalgo FJ. Use of high-resolution 13C nuclear magnetic resonance spectroscopy for the screening of virgin olive oils. J Am Oil Chem Soc. 2001;78:89–94.CrossRefGoogle Scholar
  7. 7.
    Vlahov G. 13C nuclear magnetic resonance spectroscopy to determine olive oil grades. Anal Chim Acta. 2006;577:281–7.CrossRefGoogle Scholar
  8. 8.
    Merchak N, Bejjani J, Rizk T, Silvestre V, Remaud G, Akoka S. 13C isotopomics of triacylglycerols using NMR with polarization transfer techniques. Anal Methods. 2015;7:4889–91.CrossRefGoogle Scholar
  9. 9.
    Scanoa P, Casua M, Laia A, Sabaa G, Dessib MA, Deianab M, et al. Recognition and quantitation of cis-vaccenic and eicosenoic fatty acids in olive oils by 13C nuclear magnetic resonance spectroscopy. Lipids. 1999;34:757–9.CrossRefGoogle Scholar
  10. 10.
    Vlahov G, Chepkwony PK, Ndalut PK. 13C NMR characterization of triacylglycerols of Moringa oleifera seed oil: an “oleic-vaccenic acid” oil. J Agric Food Chem. 2002;50:970–5.CrossRefGoogle Scholar
  11. 11.
    European Commission. European Community regulation no. 72/77 of 13 January 1977 Amending Regulation (EEC) No 1470/68 on the drawing and reduction of samples and the determination of oil content, impurities and moisture in oil seeds. In: EUR-Lex. 1977. http://eur-lex.europa.eu. Accessed 28 Jan 2016.
  12. 12.
    International Olive Council. COI/T.20/Doc. No 33, Determination of the fatty acid methyl esters by gas chromatography. In: IOC, Areas of activity, Chemistry, Testing methods. 2015. http://www.internationaloliveoil.org. Accessed 28 Jan 2016.
  13. 13.
    Thibaudeau C, Remaud G, Silvestre V, Akoka S. Performance evaluation of quantitative adiabatic 13C NMR pulse sequences for site-specific isotopic measurements. Anal Chem. 2010;82:5582–90.CrossRefGoogle Scholar
  14. 14.
    Bussy U, Thibaudeau C, Thomas F, Desmurs JR, Jamin E, Remaud GS, et al. Isotopic finger-printing of active pharmaceutical ingredients by 13C NMR and polarization transfer techniques as a tool to fight against counterfeiting. Talanta. 2011;85:1909–14.CrossRefGoogle Scholar
  15. 15.
    Galtier O, Dupuy N, Le Dréau Y, Ollivier D, Pinatel C, Kister J, et al. Geographic origins and compositions of virgin olive oils determinated by chemometric analysis of NIR spectra. Anal Chim Acta. 2007;595:136–44.CrossRefGoogle Scholar
  16. 16.
    Lerma-García MJ, Simó-Alfonso EF, Bendini A, Cerretani L. Rapid evaluation of oxidised fatty acid concentration in virgin olive oil using Fourier-transform infrared spectroscopy and multiple linear regression. Food Chem. 2011;124:679–84.CrossRefGoogle Scholar
  17. 17.
    Kapur GS, Ecker A, Meusinger R. Establishing quantitative structure–property relationships (QSPR) of diesel samples by proton-NMR & multiple linear regression (MLR) analysis. Energy Fuel. 2001;15:943–8.CrossRefGoogle Scholar
  18. 18.
    Tenailleau E, Akoka S. Adiabatic 1H decoupling scheme for very accurate intensity measurements in 13C NMR. J Magn Reson. 2007;1:50–8.CrossRefGoogle Scholar
  19. 19.
    Rakotomalala R. Tanagra: a free software for research and academic purposes. In: Proceedings of EGC’2005, RNTI-E-3. 2005;2:697–702.Google Scholar
  20. 20.
    Hawkins DM, Basak SC, Mills D. Assessing model fit by cross-validation. J Chem Inf Comput Sci. 2003;43:579–86.CrossRefGoogle Scholar
  21. 21.
    Gauchi JP, Chagnon P. Comparison of selection methods of explanatory variables in PLS regression with application to manufacturing process data. Chemom Intell Lab. 2001;58:171–93.CrossRefGoogle Scholar
  22. 22.
    Golbraikh A, Tropsha A. Beware of q 2! J Mol Graph Model. 2002;20:269–76.CrossRefGoogle Scholar
  23. 23.
    Roy PP, Roy K. On some aspects of variable selection for partial least squares regression models. QSAR Comb Sci. 2008;3:302–13.CrossRefGoogle Scholar
  24. 24.
    Boskou D, Blekas G, Tsimidou M. Olive oil composition. Olive oil: chemistry and technology. 1996;1996:52–83.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Noelle Merchak
    • 1
    • 2
  • Virginie Silvestre
    • 2
  • Denis Loquet
    • 2
  • Toufic Rizk
    • 1
  • Serge Akoka
    • 2
  • Joseph Bejjani
    • 1
    Email author
  1. 1.Research Unit: Technologies et Valorisation Agroalimentaire (TVA), Laboratory of Metrology and Isotopic Fractionation, Faculty of SciencesSaint-Joseph UniversityBeirutLebanon
  2. 2.EBSI team, Interdisciplinary Chemistry: Synthesis, Analysis, Modelling (CEISAM)University of Nantes-CNRS UMR 6230Nantes cedex 3France

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