PT  - JOURNAL ARTICLE
AU  - Isabel M. Moreno
AU  - Angel J. Gutiérrez
AU  - Carmen Rubio
AU  - A. Gustavo González
AU  - Dailos Gonzalez-Weller
AU  - Naouel Bencharki
AU  - Arturo Hardisson
AU  - Consuelo Revert
TI  - Classification of Spanish Red Wines by Using Artificial Neural Networks with Oenological Parameters and Mineral Content
AID  - 10.5344/ajev.2017.17021
DP  - 2018 Jan 08
TA  - American Journal of Enology and Viticulture
PG  - ajev.2017.17021
4099  - http://www.ajevonline.org/content/early/2018/01/04/ajev.2017.17021.short
4100  - http://www.ajevonline.org/content/early/2018/01/04/ajev.2017.17021.full
AB  - Spanish red wines from the Canary Islands were categorized into seven classes, and 20 samples from each DO studied, 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) were studied. Metals (B, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Ni, Na, Pb, and Zn) and physico-chemical (pH, volatile acidity, total acidity, malic acid, acetic acid, reducing sugars, alcohol content, free sulphur dioxide, total sulphur dioxide, and total polyphenols) were considered as suitable descriptors to differentiate between classes. Supervised learning pattern recognition procedures were applied. Linear discriminant analysis led to good results up to about 80% of correct classification. To improve the results, another kind of algorithms able to model non-linear separation between classes was considered: Artificial Neural Networks. Accordingly, excellent results were obtained, with 100% of the 140 wines correctly placed in the associated seven classes. Our results are in good agreement with the working hypothesis of differentiation between wines coming from different locations including different islands and also different sites in the Tenerife Island.