Evaluation of Wines Rating Based on Sensory Characteristics Using Neural Networks

Nikolas Tsakiris; Theodoros Manavis; Argyro Bekatorou

doi:10.6000/1927-3037.2016.05.04.3

Authors

Nikolas Tsakiris Department of Computer Science, Independent Studies of Science and Technology - University of Hertfordshire, 72 Pireos Str., 18346, Athens, Greece
Theodoros Manavis Department of Computer Science, Independent Studies of Science and Technology - University of Hertfordshire, 72 Pireos Str., 18346, Athens, Greece
Argyro Bekatorou Department of Chemistry, University of Patras, 26500, Patras, Greece

DOI:

https://doi.org/10.6000/1927-3037.2016.05.04.3

Keywords:

Wine rating, sensory evaluation, Feedforward Neural Networks, regression analysis.

Abstract

Wine is an agricultural product with very high commerce price variation, which is highly affected by quality ratings. Therefore, quality rating is particularly important for both industry and consumers. However, absence of clear concepts on what constitutes wine quality makes the perception of quality highly subjective, and it is usual for tasters to disagree on the quality rating of a specific wine. For this purpose, a Feedforward Neural Network (FNN) could be trained in order to predict wine quality. In this study, a new FNN method was developed to predict the accurate wine quality based on major sensory characteristics as FNN inputs, and to improve the ability of a taster, groups of tasters, or consumers, to rate wine by taking into account previous decisions. Specifically, five principle sensory characteristics of wines were used as inputs (Aging in Barrel, Aroma Intensity, Body, Astringency, and Acidity) in a rating range 1-3. As outputs, the quality ratings of wines in a range 70-100 were considered. The FNN was created in MATLAB with 1 hidden layer, 5 neurons and 1 output layer. For ratings divided in 5 categories the accuracy was 53% with the use of the FNN, as opposed to the accuracy of 36% achieved by Multiple Linear Regression. For ratings divided in 9 categories the accuracy was 90%. This method may allow each individual or group of tasters to introduce their own data to produce a more objective rating by taking into account previous decisions (subjective) that have accumulated in the database.

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