MARÍA J. MARTELO-VIDAL, MANUEL VÁZQUEZ*
*Corresponding author
University of Santiago de Compostela, Faculty of Veterinary Science, Department of Analytical Chemistry, 27002 Lugo, Spain

Abstract

Artificial neural networks (ANN) combined with ultraviolet (UV), visible (VIS) and near-infrared (NIR) spectral analysis were evaluated as a rapid method to classify wines. Wines belong to Designation of Origin (DO) Rías Baixas, Ribeira Sacra, Monterrei, Ribeiro and Valdeorras (Northwest of Spain) were classified. Classification inside a DO were also studied for subzones of DO Rías Baixas. Results showed that DO Ribeiro, Valdeorras, Monterrei and Ribeira Sacra were 100 percent classified using the ANN architectures developed. Rías Baixas were the DO with a worst classification. Inside DO Rías Baixas, Rosal was 100 percent classified. Results showed the feasibility of applying ANN and UV-VIS-NIR analysis to the authentication of DO.

INTRODUCTION

Wine composition depends of soil, culture conditions, microclimate, macroclimate and winemaking techniques (1, 2). The wine analysis is not an easy task due to its heterogeneity and complexity. The economic value of wine made the wine authentication or classification an important task worldwide. This importance is related to quality, prevention of adulterations, food safety and control of winemaking process. Wine quality in import-export markets should be also guaranteed (3). Winemakers and consumers demand analytical low-cost and effective tools to determine the quality of wine (4).
The controlled designation of origin (DO) is the name of a region or place recognized to produce foods with special characteristic regarding other places and with different manufacture and/or materials (5, 6). Wines produced in regions with DO have Regulatory Councils that control the growing and production conditions (2, 7). The authentication of wines from DO is an important task for DO Regulatory Councils. They have also the difficult task to determine the origin between the production subzones inside a DO (8).
Different classification methods ...