RT info:eu-repo/semantics/article
T1 Prediction models to control aging time in red wine
A1 Astray, Gonzalo
A1 Mejuto, Juan Carlos
A1 Martínez Martínez, Víctor
A1 Nevares Domínguez, Ignacio Gerardo
A1 Álamo Sanza, María del
A1 Simal Gandara, Jesus
K1 Wine
K1 Vino
K1 Prediction models
K1 Modelos predictivos
AB A combination of physical-chemical analysis has been used to monitor the aging of red wines from D.O. Toro (Spain). The changes in the chemical composition of wines that occur over the aging time can be used to distinguish between wine samples collected after one, four, seven and ten months of aging. Different computational models were used to develop a good authenticity tool to certify wines. In this research, different models have been developed: Artificial Neural Network models (ANNs), Support Vector Machine (SVM) and Random Forest (RF) models. The results obtained for the ANN model developed with sigmoidal function in the output neuron and the RF model permit us to determine the aging time, with an average absolute percentage deviation below 1%, so it can be concluded that these two models have demonstrated their capacity to predict the age of wine.
PB MDPI
SN 1420-3049
YR 2019
FD 2019
LK https://uvadoc.uva.es/handle/10324/56437
UL https://uvadoc.uva.es/handle/10324/56437
LA eng
NO Molecules, 2019, vol. 24, n. 5, 826
NO Producción Científica
DS UVaDOC
RD 17-jul-2024