Wine Quality Assessment with Application Specific 2D Single Channel Convolutional Neural Networks

Electronic nose is becoming a popular tool for various application areas. The data of an electronic nose is collected with various chemical sensor arrays and then odors are classified with suitable pattern recognition methods. This paper proposes a convolutional neural network for the the classification task of a wine quality electronic nose dataset. Method was tested on different portions of the dataset and compared with two previous studies. Proposed method managed to obtain high accuracy results within the relatively short time period. Additionally, method was tested by using portions of the sensor responses, hence allowing the user to assess wine quality earlier. Each training was repeated ten times in order to minimize the effects of random data selection.