Preprint Article Version 1 This version is not peer-reviewed

Prediction Models to Control Aging Time in Red Wine

Version 1 : Received: 10 January 2019 / Approved: 11 January 2019 / Online: 11 January 2019 (10:50:58 CET)

How to cite: Astray, G.; Mejuto, J.; Martinez-Martinez, V.; Nevares, I.; Alamo-Sanza, M.; Simal-Gandara, J. Prediction Models to Control Aging Time in Red Wine. Preprints 2019, 2019010110 (doi: 10.20944/preprints201901.0110.v1). Astray, G.; Mejuto, J.; Martinez-Martinez, V.; Nevares, I.; Alamo-Sanza, M.; Simal-Gandara, J. Prediction Models to Control Aging Time in Red Wine. Preprints 2019, 2019010110 (doi: 10.20944/preprints201901.0110.v1).

Abstract

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 along aging time can be permitted to discriminate wine samples collected after one, four, seven and ten months of aging. Different computational models were used to develop a good authenticity tool to certificate wines. In this research different models have 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 to determine the aging time, with an average absolute percentage deviation below 1% and it can conclude that these two models have demonstrated its capacity as a valid tool to predict the wine age.

Subject Areas

Food authenticity; Toro appellation of origin; Prediction Models; Wine; Aging.

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