title: Metallic content of wines from the Canary Islands (Spain). Application of artificial neural networks to the data analysis.
abstract: Eleven elements, K, Na, Ca, Mg, Fe, Cu, Zn, Mn, Sr, Li and Rb, were determined in dry and sweet wines bearing the denominations of origin of El Hierro, La Palma and Lanzarote islands (Canary Islands, Spain). Analyses were performed by flame atomic absorption spectrophotometry, with the exceptions of Li and Rb for which flame atomic emission spectrophotometry was used. The content in copper and iron did not present risks of cases. All samples presented a copper and zinc content below the maximum amount recommended by the Office International de la Vigne et du Vin (OIV) for these elements. Significant differences in the metallic content were found among the different islands. Thus, Lanzarote presented the highest mean content in sodium and lithium and the lowest mean content in rubidium, and La Palma presented the highest mean content in strontium and rubidium. Sweet wines from La Palma, elaborated as naturally sweet with over-ripe grapes, presented mean contents significantly higher with regard to dry wines from the same island in the majority of the analysed elements. Cluster analysis and Kohonen self-organising maps showed differences in wines according to the island of origin and the ripening state of the grapes. Back-propagation artificial neural networks showed better prediction ability than stepwise linear discriminant analysis.
authors.affiliation: Department of Analytical Chemistry, Nutrition and Food Science, University of La Laguna, Santa Cruz de Tenerife, Spain.
authors.forename: Sergio
authors.initials: S
authors.lastname: Frías
authors.forename: José E
authors.initials: JE
authors.lastname: Conde
authors.forename: Miguel A
authors.initials: MA
authors.lastname: Rodríguez
authors.forename: Vlasta
authors.initials: V
authors.lastname: Dohnal
authors.forename: Juan P
authors.initials: JP
authors.lastname: Pérez-Trujillo
mesh_terms: D001266:Atlantic Islands; D002118:Calcium / Q000032:analysis; D016000:Cluster Analysis; D003300:Copper / Q000032:analysis; D005843:Geography; D007501:Iron / Q000032:analysis; D008094:Lithium / Q000032:analysis; D008274:Magnesium / Q000032:analysis; D008345:Manganese / Q000032:analysis; D008670:Metals / Q000032:analysis*; D016571:Neural Networks, Computer; D011188:Potassium / Q000032:analysis; D012413:Rubidium / Q000032:analysis; D012964:Sodium / Q000032:analysis; D013054:Spectrophotometry, Atomic; D013324:Strontium / Q000032:analysis; D014920:Wine / Q000032:analysis*; D015032:Zinc / Q000032:analysis
chemical_list: D008670:Metals; D008345:Manganese; D003300:Copper; D008094:Lithium; D012964:Sodium; D007501:Iron; D008274:Magnesium; D015032:Zinc; D012413:Rubidium; D011188:Potassium; D002118:Calcium; D013324:Strontium