Classification of German White Wines with Certified Brand of Origin by Multielement Quantitation and Pattern Recognition Techniques
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- 作者：Maria del Mar Castiñ ; eira Gó ; mez ; Ingo Feldmann ; Norbert Jakubowski ; and Jan T. Andersson
- 刊名：Journal of Agricultural and Food Chemistry
- 出版年：2004
- 出版时间：May 19, 2004
- 年：2004
- 卷：52
- 期：10
- 页码：2962 - 2974
- 全文大小：253K
- 年卷期：v.52,no.10(May 19, 2004)
- ISSN：1520-5118
文摘
A procedure is proposed for the determination of the authenticity of white wines from four Germanwine-growing regions (Baden, Rheingau, Rheinhessen, and Pfalz) based on their content of somemajor, trace, and ultratrace elements. One hundred and twenty-seven white wine samples possessinga certificate of origin, all of the 2000 vintage, were analyzed. The concentrations of 13 elements (Li,B, Mg, Ca, V, Mn, Co, Fe, Zn, Rb, Sr, Cs, and Pb) were determined in wine diluted 1:20 by sectorfield inductively coupled plasma mass spectrometry (SF-ICP-MS). Indium was routinely used asinternal standard. Supervised pattern recognition techniques such as discriminant analysis andclassification trees were applied for the interpretation of the data. A quadratic discriminant analysis(QDA) allowed the four regions to be discriminated with 83% accuracy when using only eight variables(Li, B, Mg, Fe, Zn, Sr, Cs, and Pb), and the prediction ability for classifying new samples was 76%.By use of a second method, a decision tree, the classification of samples coming from the four regionscould be performed with an accuracy of 84% when only four elements were used: Li (very low insamples from Baden), Zn (abnormally low in the samples from the Rheingau), and Mg and Sr (bothimportant for the differentiation between Pfalz and Rheinhessen samples). For this method, theprediction ability was only 74% in the identification of unknown samples. The robustness of the QDAmodel was not good enough, and therefore the tree is better recommended for the classification ofnew wine samples from these areas of German wine production.Keywords: Wine; SF-ICP-MS; pattern recognition techniques