Differentiation of ‘two Andalusian DO ‘fino’ wines according to their metal content from ICP-OES by using supervised pattern recognition methods

Abstract The metal content (Ca, Mg, Sr, Ba, K, Na, P, Fe, Al, Mn, Cu and Zn) of several ‘fino’ wines belonging to two Andalusian Denomination of Origin (DO) was determined by ICP-OES. Metal concentrations were selected as chemical descriptors for discrimination, because they play a primary role in the discrimination due to its correlation with soil nature, geographical origin and grape variety. The two studied Andalusian DO were the Jerez-Xeres-Sherry & Manzanilla-Sanlucar de Barrameda (class D) and the Condado de Huelva (class C). Linear Discriminant Analysis (LDA) and procedures based on Artificial Neural Networks (ANN) leads to a perfect separation of classes, especially when applying Multi Layer Perceptrons ANN trained by back-propagation.

[1]  A. G. González,et al.  Pattern recognition procedures for differentiation of Green, Black and Oolong teas according to their metal content from inductively coupled plasma atomic emission spectrometry. , 2001, Talanta.

[2]  D. Coomans,et al.  Optimization by statistical linear discriminant analysis in analytical chemistry , 1979 .

[3]  M. Ortega-Heras,et al.  Comparative study of artificial neural network and multivariate methods to classify Spanish DO rose wines. , 2004, Talanta.

[4]  Bruce R. Kowalski,et al.  Pattern recognition analysis of gas chromatographic data. Geographic classification of wines of Vitis vinifera cv Pinot Noir from France and the United States , 1980 .

[5]  Ioannis S. Arvanitoyannis,et al.  Application of quality control methods for assessing wine authenticity : Use of multivariate analysis (chemometrics) , 1999 .

[6]  J. Pérez-Trujillo,et al.  Classification of commercial wines from the Canary Islands (Spain) by chemometric techniques using metallic contents. , 2003, Talanta.

[7]  G. López-Pérez,et al.  Holmes, a program for performing Procrustes Transformations , 2001 .

[8]  Á. Jos,et al.  Differentiation of sparkling wines (cava and champagne) according to their mineral content. , 2004, Talanta.

[9]  E. Peña,et al.  Classification and differentiation of bottled sweet wines of Canary Islands (Spain) by their metallic content , 2001 .

[10]  Henk Maarse,et al.  Classification of wines according to type and region based on their composition , 1987 .

[11]  Carlos Herrero,et al.  Pattern recognition analysis applied to classification of wines from Galicia (northwestern Spain) with certified brand of origin , 1994 .

[12]  D. Coomans,et al.  The application of linear discriminant analysis in the diagnosis of thyroid diseases , 1978 .

[13]  M. Suchanek,et al.  Multivariate classification of wines from different bohemian regions (Czech Republic) , 2005 .

[14]  D. Gonzalez-Arjona,et al.  Non-linear QSAR modeling by using multilayer perceptron feedforward neural networks trained by back-propagation. , 2002, Talanta.

[15]  Bruce R. Kowalski,et al.  Pattern recognition analysis of elemental data. Wines of Vitis vinifera cv Pinot Noir from France and the United States , 1979 .