2026-04-22T23:19:11Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/564372022-10-21T19:18:31Zcom_10324_1166com_10324_931com_10324_894col_10324_1338

Astray, Gonzalo Mejuto, Juan Carlos Martínez Martínez, Víctor Nevares Domínguez, Ignacio Gerardo Álamo Sanza, María del Simal Gandara, Jesus 2022-10-21T11:55:06Z 2022-10-21T11:55:06Z 2019 Molecules, 2019, vol. 24, n. 5, 826 1420-3049 https://uvadoc.uva.es/handle/10324/56437 10.3390/molecules24050826 Producción Científica 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. Programa de Cooperación Interreg V-A España–Portugal (POCTEP) 2014-2020 (project 0377_IBERPHENOL_6_E) Xunta de Galicia (postdoctoral grant POS-B/2016/001) application/pdf eng MDPI info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ © 2019 The Authors Atribución 4.0 Internacional Wine Vino Prediction models Modelos predictivos Prediction models to control aging time in red wine info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://www.mdpi.com/1420-3049/24/5/826 SI