Application of multivariate analysis and artificial neural networks for the differentiation of red wines from the Canary Islands according to the island of origin
Application of multivariate analysis and artificial neural networks for the differentiation of red wines from the Canary Islands according to the island of origin
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DOI：
10.1021/jf0343581
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发表时间：
2003-07-16
影响因子：
6.1
通讯作者：
Trujillo, JPP
中科院分区：
文献类型：
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作者：
Díaz, C;Conde, JE;Trujillo, JPP
在来自加那利群岛的 83 种红酒中测定了 11 种金属（K、Na、Ca、Mg、Fe、Cu、Zn、Mn、Sr、Li 和 Rb）。这些葡萄酒含有高浓度的Na，而Cu和Zn的浓度远低于国际葡萄与葡萄酒办公室（OIV）规定的最高浓度。应用主成分分析，维数空间减少到五个主成分，解释了总方差的 76.4%，并且葡萄酒倾向于根据生产岛进行分离。线性判别分析（LDA）可以根据生产岛对葡萄酒进行合理的分类。当人工神经网络（Kohonen 自组织图和反向传播前馈分别作为无监督和监督技术）应用于由分析的金属构成的数据矩阵时，结果相对于通过其他多元方法获得的结果有所改善，这些方法根据生产岛观察葡萄酒的差异。
Eleven metals (K, Na, Ca, Mg, Fe, Cu, Zn, Mn, Sr, Li, and Rb) were determined in 83 red wines from the Canary Islands. The wines presented high concentrations of Na, and the concentrations of Cu and Zn were much lower than the maximum concentrations established by the International Office of Vine and Wine (OIV). Applying principal component analysis, the dimension space was reduced to five principal components that explain 76.4% of the total variance, and the wines tend to separate on the basis of the island of production. Linear discriminant analysis (LDA) allowed a reasonable classification of wines according to the island of production. When artificial neural networks (Kohonen self-organizing maps and back-propagation feed-forward as unsupervised and supervised techniques, respectively) were applied on the matrix of data constituted by the analyzed metals, the results improved in relation to those obtained by other multivariate methods observing a differentiation of wines according to island of production.