European Food Research and Technology

, Volume 231, Issue 5, pp 733–743 | Cite as

Multivariate discrimination of wines with respect to their grape varieties and vintages

  • A. J. Charlton
  • M. S. Wrobel
  • I. Stanimirova
  • M. Daszykowski
  • H. H. Grundy
  • B. Walczak
Original Paper

Abstract

The primary focus of the European Union funded project entitled “Establishing a WINE Data Bank for analytical parameters for wines from Third Countries” (WINE-DB project, G6RD-CT-2001-00646-WINE-DB) was the discrimination of wine samples with respect to their geographical origin using only a few chemical parameters. Taking a step further, we have investigated the possibility of discriminating the wines in the data bank according to their harvesting seasons and grape varieties. Several chemometric methods were carefully selected and evaluated for this purpose. These were discriminant partial least squares, classification and regression trees, uninformative variable elimination discriminant partial least squares and neuro-fuzzy systems. With classification and regression trees, it was possible to identify a few chemical parameters including isotopic ratios (e.g. δ18O), biogenic amines and rare earth elements that discriminate between vintages and some grape varieties for wines produced in a particular country such as Czech Republic, Hungary, Romania or South Africa. These parameters can be used in evaluating the authenticity of wines.

Keywords

Chemometrics Wine Variable selection Monte Carlo validation 

Notes

Acknowledgments

The authors would like to thank The UK Food Standards Agency: Standards, Authenticity and Food Law Policy Branch for funding (project number Q01124). The project consortium would also like to thank the European Commission for funding the WINE DB project (Contract No.: G6RD-CT-2001-00646-WINE-DB). The authors are solely responsible for the content of this research article and the European Community are not responsible for any use that might be made of the data appearing therein. Additionally we thank R. Wittkowski and C. Fauhl-Hassek (BfR, Germany), P. Brereton (Fera, U.K), C. Guillou (JRC-Ispra, Italy), B. Medina (Directeur du Laboratoire de Bordeaux/Talence, France), M. Woolfe (formerly FSA, UK) and A. Blanch (Ministerio de medio Ambiente y medio Ruraly Marino, Spain) for their valued advice. Useful editorial comments were provided by Dr L. Castle (Fera) who we also thank.

References

  1. 1.
    Capron X, Smeyers-Verbeke J, Massart D (2007) Food Chem 101:1585–1597CrossRefGoogle Scholar
  2. 2.
    Câmara J, Alves M, Marques J (2006) Talanta 68:1512–1521CrossRefGoogle Scholar
  3. 3.
    Roussel S, Bellon-Maurel V, Roger J, Grenier P (2003) J Food Eng 60:407–419CrossRefGoogle Scholar
  4. 4.
    Forina M, Casale M, Oliveri P (2009) Application of chemometrics to food chemistry. In: Brown SD, Tauler R, Walczak B (eds) Comprehensive chemometrics. Elsevier, Amsterdam, pp 75–128CrossRefGoogle Scholar
  5. 5.
    Forina M, Armanino C, Castino M, Ubigli M (1986) Vitis 25:189–201Google Scholar
  6. 6.
    Coetzee P, Vanhaecke F (2005) Anal Bioanal Chem 383:977–984CrossRefGoogle Scholar
  7. 7.
    Martin G, Mazure M, Jouitteau C, Martin Y, Aguile L, Allain P (1999) Am J Enol Viticult 50:409–417Google Scholar
  8. 8.
    Giménez-Miralles J, Salazar D, Solana I (1999) J Agric Food Chem 47:2645–2652CrossRefGoogle Scholar
  9. 9.
    Barbaste M, Robinson K, Guilfoyle S, Medina B, Lobinski R (2002) J Anal Atomic Spectrom 17:135–137CrossRefGoogle Scholar
  10. 10.
    Baxter M, Crews H, Dennis M, Goodall I, Anderson D (1997) Food Chem 60:443–450CrossRefGoogle Scholar
  11. 11.
    Suhaj M, Koreňovská M (2005) Acta Aliment 34:393–401CrossRefGoogle Scholar
  12. 12.
    Cortell J, Halbleib M, Gallagher A, Righetti T, Kennedy J (2005) J Agric Food Chem 53:5798–5808CrossRefGoogle Scholar
  13. 13.
    Russell K, Zivanovic S, Morris W, Penfield M, Weiss J (2005) J Food Qual 28:377–385CrossRefGoogle Scholar
  14. 14.
    Picque D, Cattenoz T, Corrieu G, Berger J (2005) Sci des Aliment 25:207–220CrossRefGoogle Scholar
  15. 15.
    Martí M, Busto O, Guasch J (2004) J Chromatogr A 1057:211–217CrossRefGoogle Scholar
  16. 16.
    Fischer U, Roth D, Christmann M (1999) Food Qual Preference 10:281–288CrossRefGoogle Scholar
  17. 17.
    Smeyers-Verbeke J, Jäger H, Lanteri S, Brereton P, Jamin E, Fauhl-Hassek C, Forina M, Römisch U (2009) Eur Food Res Technol 230:15–29CrossRefGoogle Scholar
  18. 18.
    Römisch U, Jäger H, Capron X, Lanteri S, Forina M, Smeyers-Verbeke J (2009) Eur Food Res Technol 230:31–45CrossRefGoogle Scholar
  19. 19.
    Forina M, Oliveri P, Jäger H, Römisch U, Smeyers-Verbeke J (2009) Chemom Intell Lab Syst 99:127–137CrossRefGoogle Scholar
  20. 20.
    Schlesier K, Fauhl-Hassek C, Forina M, Cotea V, Kocsi E, Schoula R, van Jaarsveld F, Wittkowski R (2009) Eur Food Res Technol 230:1–13CrossRefGoogle Scholar
  21. 21.
    Martens H, Næs T (1989) Multivariate calibration. Wiley, ChichesterGoogle Scholar
  22. 22.
    Centner V, Massart D, De Noord O, De Jong S, Vandeginste B, Sterna C (1996) Anal Chem 68:3851–3858CrossRefGoogle Scholar
  23. 23.
    Guyon I, Gunn S, Nikravesh M, Zadeh L (2006) Feature extraction, foundations and applications. Springer, BerlinCrossRefGoogle Scholar
  24. 24.
    Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and regression trees. Wadsworth International Group, Belmont, CAGoogle Scholar
  25. 25.
    Jang J, Sun C, Mizutani E (1997) Neuro fuzzy and soft computing. Prentice-Hall, Englewood Cliffs, USAGoogle Scholar
  26. 26.
    Walczak B, Wegscheider W (1994) Chemom Intell Lab Syst 22:199–207CrossRefGoogle Scholar
  27. 27.
    Walczak B, Bauer-Wolf E, Wegscheider W (1994) Mikrochimica Acta 113:153–169CrossRefGoogle Scholar
  28. 28.
    Kennard R, Stone L (1969) Technometrics 11:137–148CrossRefGoogle Scholar
  29. 29.
    Snee R (1977) Technometrics 19:415–428CrossRefGoogle Scholar
  30. 30.
    Westerhuis J, Hoefsloot H, Smit S, Vis D, Smilde A, Velzen E, Duijnhoven J, Dorsten F (2008) Metabolomics 4:81–89CrossRefGoogle Scholar
  31. 31.
    Stanimirova I, Walczak B, Massart DL, Simeonov V (2004) Chemom Intell Lab Syst 71:83–95CrossRefGoogle Scholar
  32. 32.
    Daszykowski M, Kaczmarek K, Vander Heyden Y, Walczak B (2007) Chemom Intell Lab Syst 85:203–219CrossRefGoogle Scholar

Copyright information

© Her Majesty the Queen in Rights of the United Kingdom 2010

Authors and Affiliations

  • A. J. Charlton
    • 1
  • M. S. Wrobel
    • 2
  • I. Stanimirova
    • 2
  • M. Daszykowski
    • 2
  • H. H. Grundy
    • 1
  • B. Walczak
    • 2
  1. 1.Food and Environment Research AgencySand HuttonUK
  2. 2.Department of Chemometrics, Institute of ChemistryThe University of SilesiaKatowicePoland

Personalised recommendations