Prediction Models to Control Aging Time in Red Wine

Molecules : a Journal of Synthetic Chemistry and Natural Product Chemistry
Gonzalo AstrayJesus Simal-Gandara

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 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.

References

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Citations

Oct 9, 2020·Critical Reviews in Food Science and Nutrition·Zhilong YuJuhong Chen
Feb 13, 2021·Molecules : a Journal of Synthetic Chemistry and Natural Product Chemistry·Javier EchaveJesus Simal-Gandara
Jun 4, 2020·Ultrasonics Sonochemistry·Katarina LukićKarin Kovačević Ganić

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Methods Mentioned

BETA
chips
atomic absorption spectroscopy

Software Mentioned

RapidMiner
LIBSVM
EasyNN plus
Excel
Office Professional Plus
Sigmaplot
Neural Planner
Systat
Random
RapidMiner Studio

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