Classification of Spanish Red Wines Using Artificial Neural Networks with Enological Parameters and Mineral Content
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Isabel M. Moreno ; Angel J. Gutiérrez [2] ; Carmen Rubio [2] ; A. Gustavo González [1] ; Dailos Gonzalez-Weller [3] ; Naouel Bencharki [2] ; Arturo Hardisson [2] ; Consuelo Revert [2]
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[1] Universidad de Sevilla
Universidad de Sevilla
Sevilla, España
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[2] Universidad de La Laguna
Universidad de La Laguna
San Cristóbal de La Laguna, España
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[3] Canarian Public Health Service
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- Localización: American Journal of Enology and Viticulture, ISSN 0002-9254, Vol. 69, Nº 2, 2018, págs. 167-175
- Idioma: inglés
- Texto completo no disponible (Saber más ...)
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Resumen
Spanish red wines from the Canary Islands were categorized into seven classes: Tacoronte-Acentejo (class T), Valle de la Orotava (class O), Ycoden-Daute-Isora (class YDI), Abona (class A), Valle de Güímar (class VG), La Gomera (class G), and La Palma (class P), and 20 samples were studied from each denomination of origin. Metal concentrations (B, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Ni, Na, Pb, and Zn) and physicochemical parameters (pH, volatile acidity, total acidity, malic acid, acetic acid, reducing sugars, alcohol content, free sulfur dioxide, total sulfur dioxide, and total polyphenols) were used as descriptors to differentiate among classes. Supervised learning pattern recognition procedures were applied. Linear discriminant analysis allowed up to ~80% of correct classification. To improve discriminatory accuracy, another kind of algorithm that can model nonlinear separation among classes was considered: artificial neural networks. This method obtained excellent results, with 100% of the 140 wines correctly placed into the associated seven classes. Our results are in good agreement with the working hypothesis of differentiation among wines coming from different locations, including both different islands and different sites on Tenerife Island.
