RT Journal Article
SR Electronic
T1 Classification of Spanish Red Wines Using Artificial Neural Networks with Enological Parameters and Mineral Content
JF American Journal of Enology and Viticulture
JO Am J Enol Vitic.
FD American Society for Enology and Viticulture
SP 167
OP 175
DO 10.5344/ajev.2017.17021
VO 69
IS 2
A1 Isabel M. Moreno
A1 Angel J. Gutiérrez
A1 Carmen Rubio
A1 A. Gustavo González
A1 Dailos Gonzalez-Weller
A1 Naouel Bencharki
A1 Arturo Hardisson
A1 Consuelo Revert
YR 2018
UL http://www.ajevonline.org/content/69/2/167.abstract
AB 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.