A Review of the Application of Electronic Nose Combined with Deep Learning in Wine Quality Detection

Jiayu Huang

doi:10.54254/2755-2721/2025.BJ25230

Applied and Computational EngineeringOpen access

A Review of the Application of Electronic Nose Combined with Deep Learning in Wine Quality Detection

Research Article

Open Access

A Review of the Application of Electronic Nose Combined with Deep Learning in Wine Quality Detection

Jiayu Huang ^1*

¹ School of Information Science and Technology, Beijing University Of Technology, No. 100 Ping Leyuan, Chaoyang District, Beijing, China

^*Corresponding author: 3233822484@qq.com

Published on 20 July 2025

ACE Vol.177

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-241-6

ISBN (Online): 978-1-80590-242-3

Download Cover

Abstract

Inspired by biological olfaction, the electronic nose is a modern detection instrument that mimics the structure and function of the olfactory system. It has been successfully applied in various fields, including industry and food testing. Wine is a globally beloved alcoholic beverage, and the technology used for wine quality detection plays a crucial role in ensuring the healthy development of related industries. The use of electronic noses in wine detection offers potential advantages such as rapid analysis and portability. This paper provides a comprehensive review of research on wine aroma detection using electronic noses, explores deep learning methods for processing e-nose data, and discusses the application and future prospects of combining electronic noses with deep learning in the wine industry.

Keywords:

Wine detection, Odor recognition, Electronic nose, Deep learning

View PDF

References

[1]. Li Y, Tan C S, Du J Q, Fang L K. Research progress of bionic olfactory systems [J]. Journal of Functional Materials and Devices, 2022, 28(05): 401-408.

[2]. Vasiliki S. Assessment of volatile aromatic compounds in smoke tainted cabernet sauvignon wines using a low-cost e-nose and machine learning modelling [J]. Molecules, 2021, 26(16): 5108.

[3]. Margherita M. Differentiation through E-nose and GC-FID data modeling of rosé sparkling wines elaborated via traditional and Charmat methods [J]. Journal of the Science of Food and Agriculture, 2023, 105(3): 1439-1447.

[4]. Stefano P. Identifying wine grape aromatic maturity using e-nose and GC-MS: the case of nerello mascalese grapes from two contrade of the etna area [J]. OENO One, 2024, 58.

[5]. Sergio L S J. Discrimination analysis of wines made from four species of blueberry through their olfactory signatures using an e-nose [J]. Food Science & Technology, 2023, 187: 115320

[6]. Zhu H Z, Chen P H, Cai G L. Research on application of relationship mining into red wine data based on LDA model [J]. Computer Engineering and Applications, 2019, 55(4): 148-153.

[7]. Wang Q. Research on novel electronic nose gas recognition algorithm based on convolutional neural network [D]. JiangSu: China University of Mining and Technology, 2024.

References

[1]. Li Y, Tan C S, Du J Q, Fang L K. Research progress of bionic olfactory systems [J]. Journal of Functional Materials and Devices, 2022, 28(05): 401-408.

[2]. Vasiliki S. Assessment of volatile aromatic compounds in smoke tainted cabernet sauvignon wines using a low-cost e-nose and machine learning modelling [J]. Molecules, 2021, 26(16): 5108.

[4]. Stefano P. Identifying wine grape aromatic maturity using e-nose and GC-MS: the case of nerello mascalese grapes from two contrade of the etna area [J]. OENO One, 2024, 58.

[5]. Sergio L S J. Discrimination analysis of wines made from four species of blueberry through their olfactory signatures using an e-nose [J]. Food Science & Technology, 2023, 187: 115320

[6]. Zhu H Z, Chen P H, Cai G L. Research on application of relationship mining into red wine data based on LDA model [J]. Computer Engineering and Applications, 2019, 55(4): 148-153.

[7]. Wang Q. Research on novel electronic nose gas recognition algorithm based on convolutional neural network [D]. JiangSu: China University of Mining and Technology, 2024.

Cite this article

Huang,J. (2025). A Review of the Application of Electronic Nose Combined with Deep Learning in Wine Quality Detection. Applied and Computational Engineering,177,26-30.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-MLA 2025 Symposium: Applied Artificial Intelligence Research

ISBN: 978-1-80590-241-6(Print) / 978-1-80590-242-3(Online)

Editor: Hisham AbouGrad

Conference website: https://2025.confmla.org/

Conference date: 3 September 2025

Series: Applied and Computational Engineering

Volume number: Vol.177

ISSN: 2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:

1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.

2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.

3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).