Detail publikace

Elemental analysis as a tool for classification of Czech white wines with respect to grape varieties

Originální název

Elemental analysis as a tool for classification of Czech white wines with respect to grape varieties

Anglický název

Elemental analysis as a tool for classification of Czech white wines with respect to grape varieties

Jazyk

en

Originální abstrakt

The proportion of adulterated wines on the market is globally rising and this trend is also visible in the Czech Republic. The control authorities are confronted with an increasing number of cases of adulterated wine. The characteristic feature of wine growing in the Czech Republic is the use of a diverse spectrum of the wine cultivars. A varietal authenticity verification of wine is, beside the verification of geographic origin, the toughest challenge for analytical chemists and control laboratories. The aim of this study was the evaluation of possibilities of discrimination and classification of Moravian varietal wines based on elemental composition data. Important objective was to find the variables in elemental composition which are strongly associated with a particular variety. Testing was performed on three popular and often growned varieties (Rhine Riesling, Müller-Thurgau and Green Veltliner). Analysis of wine samples was carried out by the combination of ICP-MS and ICP-OES methods. Experimental data were evaluated by univariate and multivariate statistical techniques such as analysis of variance, principal component analysis and discriminant analysis. Statistically significant discriminant fuctions and predictive functions were constructed by the method of canonical discriminant analysis. These fuctions were based on elemental composition parameters Al, Sn, Gd, Tb, Tm/Yb, Yb/Lu, Mo/Sn, Mn/Cr. Created model was able to classify known varietal wines with succes rate of 95.83 %. A predictive capability of the model was finally tested by cross validation method. Classification effectivity for the unknown samples was determined to 70.83 %. Results from this study proved, that presented wine varietal authentification approach is promissing for interregional varietal wine discrimination.

Anglický abstrakt

The proportion of adulterated wines on the market is globally rising and this trend is also visible in the Czech Republic. The control authorities are confronted with an increasing number of cases of adulterated wine. The characteristic feature of wine growing in the Czech Republic is the use of a diverse spectrum of the wine cultivars. A varietal authenticity verification of wine is, beside the verification of geographic origin, the toughest challenge for analytical chemists and control laboratories. The aim of this study was the evaluation of possibilities of discrimination and classification of Moravian varietal wines based on elemental composition data. Important objective was to find the variables in elemental composition which are strongly associated with a particular variety. Testing was performed on three popular and often growned varieties (Rhine Riesling, Müller-Thurgau and Green Veltliner). Analysis of wine samples was carried out by the combination of ICP-MS and ICP-OES methods. Experimental data were evaluated by univariate and multivariate statistical techniques such as analysis of variance, principal component analysis and discriminant analysis. Statistically significant discriminant fuctions and predictive functions were constructed by the method of canonical discriminant analysis. These fuctions were based on elemental composition parameters Al, Sn, Gd, Tb, Tm/Yb, Yb/Lu, Mo/Sn, Mn/Cr. Created model was able to classify known varietal wines with succes rate of 95.83 %. A predictive capability of the model was finally tested by cross validation method. Classification effectivity for the unknown samples was determined to 70.83 %. Results from this study proved, that presented wine varietal authentification approach is promissing for interregional varietal wine discrimination.

BibTex


@article{BUT145920,
  author="Jaromír {Pořízka} and Pavel {Diviš} and Miloš {Dvořák}",
  title="Elemental analysis as a tool for classification of Czech white wines with respect to grape varieties",
  annote="The proportion of adulterated wines on the market is globally rising and this trend is also visible
in the Czech Republic. The control authorities are confronted with an increasing number of
cases of adulterated wine. The characteristic feature of wine growing in the Czech Republic is
the use of a diverse spectrum of the wine cultivars. A varietal authenticity verification of wine is,
beside the verification of geographic origin, the toughest challenge for analytical chemists and
control laboratories. The aim of this study was the evaluation of possibilities of discrimination
and classification of Moravian varietal wines based on elemental composition data. Important
objective was to find the variables in elemental composition which are strongly associated with a
particular variety. Testing was performed on three popular and often growned varieties (Rhine
Riesling, Müller-Thurgau and Green Veltliner). Analysis of wine samples was carried out by the
combination of ICP-MS and ICP-OES methods. Experimental data were evaluated by
univariate and multivariate statistical techniques such as analysis of variance, principal
component analysis and discriminant analysis. Statistically significant discriminant fuctions and
predictive functions were constructed by the method of canonical discriminant analysis. These
fuctions were based on elemental composition parameters Al, Sn, Gd, Tb, Tm/Yb, Yb/Lu,
Mo/Sn, Mn/Cr. Created model was able to classify known varietal wines with succes rate of 95.83
%. A predictive capability of the model was finally tested by cross validation method.
Classification effectivity for the unknown samples was determined to 70.83 %. Results from this
study proved, that presented wine varietal authentification approach is promissing for
interregional varietal wine discrimination.",
  chapter="145920",
  doi="10.5601/jelem.2017.22.4.1379",
  number="2",
  volume="23",
  year="2018",
  month="february",
  pages="709--727",
  type="journal article in Web of Science"
}