Parvin BulucuNalan ǑzkurtCüneyt GüzelişOsman YıldızBulucu, PervinGuzelis, CüneytYildiz, OsmanÖzkurt, Nalan2025-10-062021978605011437910.23919/ELECO54474.2021.96777672-s2.0-85125260498https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125260498&doi=10.23919%2FELECO54474.2021.9677767&partnerID=40&md5=23d203afc19944733fb38f0b3230c739https://gcris.yasar.edu.tr/handle/123456789/9039https://doi.org/10.23919/ELECO54474.2021.9677767Electronic 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. © 2022 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessBiomedical Engineering, Classification (of Information), Convolution, Convolutional Neural Networks, Pattern Recognition, Wine, Application Area, Application Specific, Chemical Sensor Arrays, Classification Tasks, Classifieds, Convolutional Neural Network, Pattern Recognition Method, Quality Assessment, Single Channels, Wine Quality, Electronic NoseBiomedical engineering, Classification (of information), Convolution, Convolutional neural networks, Pattern recognition, Wine, Application area, Application specific, Chemical sensor arrays, Classification tasks, Classifieds, Convolutional neural network, Pattern recognition method, Quality assessment, Single channels, Wine quality, Electronic noseConference Object