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Jr, R.A., de Sousa, H.C., Malmegrim, R.R., dos Santos Jr., D.S., Carvalho, A.C.P.L.F., Fonseca, F.J., Oliveira Jr., O.N. and Mattoso, L.H.C. (2004) Wine Classification by Taste Sensors Made from Ultra-Thin Films and Using Neural Networks. Sensors and Actuators B: Chemical, 98, 77-82.
https://doi.org/10.1016/j.snb.2003.09.025

has been cited by the following article:

• TITLE: Prediction of Wine Quality Using Machine Learning Algorithms

  AUTHORS: K. R. Dahal, J. N. Dahal, H. Banjade, S. Gaire

  KEYWORDS: Wine Quality, Neural Network, Machine Learning (ML), Artificial Intelligence (AI)

  JOURNAL NAME: Open Journal of Statistics, Vol.11 No.2, March 18, 2021

  ABSTRACT: As a subfield of Artificial Intelligence (AI), Machine Learning (ML) aims to understand the structure of the data and fit it into models, which later can be used in unseen data to achieve the desired task. ML has been widely used in various sectors such as in Businesses, Medicine, Astrophysics, and many other scientific problems. Inspired by the success of ML in different sectors, here, we use it to predict the wine quality based on the various parameters. Among various ML models, we compare the performance of Ridge Regression (RR), Support Vector Machine (SVM), Gradient Boosting Regressor (GBR), and multi-layer Artificial Neural Network (ANN) to predict the wine quality. Multiple parameters that determine the wine quality are analyzed. Our analysis shows that GBR surpasses all other models’ performance with MSE, R, and MAPE of 0.3741, 0.6057, and 0.0873 respectively. This work demonstrates, how statistical analysis can be used to identify the components that mainly control the wine quality prior to the production. This will help wine manufacturer to control the quality prior to the wine production.