Wine authentication: a fingerprinting multiclass strategy to classify red varietals through profound chemometric analysis of volatiles
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The verification of the grape variety with chemical–analytical methods is one of the major challenges in wine authentication. Such strategies use multivariate data analysis and are expected to separate individual grape varieties; also, the classification models for a large number of varieties shall give accurate predictions. In the part II of a non-targeted fingerprinting study presented herein, special multiclass chemometric strategies for the classification of German and non-German red wine varieties available on the German market were tested. The obtained three-dimensional raw data of a standardised headspace solid phase microextraction (HS-SPME) online coupled with gas chromatography mass spectrometry (GC–MS) was used; a metabolomics software and data pre-treatment were applied. The feasibility of the approaches was determined with four botanical origins by testing the models with external samples (validation). In particular, suitable modelling of similar wine varieties was a discriminant strategy using one-versus-one models based on orthogonal partial least squares discriminant analysis under the direction of a decision tree: on average, 85–98% correct classification of external test samples through ten tests was achieved. In addition, soft independent modelling of class analogies confirmed the classification. Both statistical strategies may be recommended for further improving wine authentication.
KeywordsFraud Dornfelder Shiraz Carmenére Merlot ROC curve analysis
The author has conducted the study at the Federal Institute for Risk Assessment (BfR), Department Safety in the Food Chain, Berlin, Germany. BfR expressely authorised its publication through the author. The author acknowledges the contributions and dedicated support of Dr. Carsten Fauhl-Hassek, BfR, and Prof. Dr. Marcus Glomb, Martin-Luther-University, Halle-Wittenberg, Germany.
Compliance with ethical standards
Conflict of interest
The author declares that there is no conflict of interest.
Compliance with ethics requirements
This study was funded by the German Federal Institute for Risk Assessment (BfR).
- 1.Regulation (EC) (2002) No 178/2002 of the European Parliament and of the Council of 28 January 2002 laying down the general principles and requirements of food law, establishing the European Food SafetyAuthority and laying down procedures in matters of food safety. OJ L 31:1–24Google Scholar
- 5.Bouloumpasi E, Soufleros EH, Tsachopoulos C, Biliarderis CG (2002) Primary amino acid composition and its use in discrimination of Greek red wines with regard to variety and cultivation region. Vitis 41:195–202Google Scholar
- 8.The International Organisation of Vine and Wine (OIV) (2007) Determination of nine major anthocyanins in red and rosé wines using HPLC. OIV-MA-AS315-11 (Oeno 22/2003, Oeno 12/2007). In: Compendium of international methods of analysis of wines and musts, vol 2. http://www.oiv.int/public/medias/2540/oiv-maas315-11.pdf. Accessed Aug 2018
- 9.Godelmann R, Fang F, Humpher E, Schütz B, Bansbach M, Schäfer H, Spraul M (2013) Targeted and nontargeted wine analysis by 1H NMR spectroscopy combined with multivariate statistical analysis. Differentiation of important parameters: grape variety, geographical origin, year of vintage. J Agric Food Chem 61:5610–5619CrossRefGoogle Scholar
- 13.Springer AE, Riedl J, Esslinger S, Roth T, Glomb MA, Fauhl-Hassek C (2014) Validated modeling for German white wine varietal authentication based on headspace solid-phase microextraction online coupled with gas chromatography mass spectrometry fingerprinting. J Agric Food Chem 62:6844–6851CrossRefGoogle Scholar
- 16.Vaclavik L, Lacina O, Hajslova J, Zweigenbaum J (2011) The use of high performance liquid chromatography-quadrupole time-of-flight mass spectrometry coupled to advanced data mining and chemometric tools for discrimination and classification of red wines according to their variety. Anal Chim Acta 685:45–51CrossRefGoogle Scholar
- 29.Rapp A (1995) Possibilities of characterizing wine varieties by means of volatile flavor compounds. In: Charalambous G (ed) Food flavors: generation, analysis and process influence. Elsevier Science, AmsterdamGoogle Scholar
- 30.German Wines Statistics 2015/2016 (2015) Deutsches Weininstitut, Bodenheim. http://www.germanwineusa.com/download/pdf/wine-statistics-2015-2016.pdf. Accessed 1 Oct 2015
- 31.Eriksson L, Johansson E, Kettaneh-Wold N, Trygg J, Wikström C, Wold S (2006) Multi- and megavariate data analysis. Part I basic principles and applications. Umetrics AB, UmeåGoogle Scholar
- 37.Eriksson L, Johansson E, Kettaneh-Wold N, Trygg J, Wikström C, Wold S (2006) Multi- and megavariate data analysis. Part II advanced applications and method extensions. Umetrics AB, UmeåGoogle Scholar
- 38.Beltrán N, Duarte-Mermoud MA, Muñoz RE (2009) Geographical classification of Chilean wines by an electronic nose. Int J Wine Res 1:209–219Google Scholar
- 40.Comission Regulation (EC) (2009) No 607/2009 of 14 July 2009 laying down certain detailed rules for the implementation of Council Regulation (EC) No 479/2008 as regards protected designations of origin and geographical indications, traditional terms, labelling and presentation of certain wine sector products. OJ L 193: 60Google Scholar
- 42.Clarke O, Rand M (2010) Grapes and wines. A comprehensive guide to varieties and flavours. Sterling Publishing, New YorkGoogle Scholar