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Open AccessArticle

Prediction Models to Control Aging Time in Red Wine

1
Department of Physical Chemistry, Faculty of Food Science and Technology, University of Vigo, Ourense Campus, 32004 Ourense, Spain
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Department of Agricultural and Forestry Engineering, UVaMOX-University of Valladolid, Palencia Campus, 34001 Palencia, Spain
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Department of Analytical Chemistry, UVaMOX-University of Valladolid, Palencia Campus, 34001 Palencia, Spain
4
Nutrition and Bromatology Group, Department of Analytical and Food Chemistry, Faculty of Food Science and Technology, University of Vigo, Ourense Campus, 32004 Ourense, Spain
*
Authors to whom correspondence should be addressed.
Molecules 2019, 24(5), 826; https://doi.org/10.3390/molecules24050826
Received: 10 January 2019 / Revised: 5 February 2019 / Accepted: 21 February 2019 / Published: 26 February 2019
(This article belongs to the Section Analytical Chemistry)
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. View Full-Text
Keywords: food authenticity; toro appellation of origin; prediction models; wine; aging food authenticity; toro appellation of origin; prediction models; wine; aging
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Astray, G.; Mejuto, J.C.; Martínez-Martínez, V.; Nevares, I.; Alamo-Sanza, M.; Simal-Gandara, J. Prediction Models to Control Aging Time in Red Wine. Molecules 2019, 24, 826.

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