A machine learning-based electronic nose system using numerous low-cost gas sensors for real-time alcoholic beverage classification-Reference-Cited by-同舟云学术

A machine learning-based electronic nose system using numerous low-cost gas sensors for real-time alcoholic beverage classification

Published:2024 Issue:34 Volume:16 Page:5909-5919
ISSN:1759-9660
Container-title:Analytical Methods
language:en
Short-container-title:Anal. Methods

Author:

Lee Sang Woo¹²^ORCID,Yoon Jeong Ah³,Kim Myoung Dong⁴,Kim Byeong Hee¹²,Seo Young Ho¹²^ORCID

Affiliation:

1. Department of Smart Health Science and Technology, Kangwon National University, Chuncheon, Gangwon-do, Republic of Korea

2. Department of Mechatronics Engineering, Kangwon National University, Chuncheon, Gangwon-do, Republic of Korea

3. Department of Food Biotechnology and Environmental Science, Kangwon National University, Chuncheon, Gangwon-do, Republic of Korea

4. Department of Food Science and Biotechnology, Kangwon National University, Chuncheon, Gangwon-do, Republic of Korea

Abstract

This study presents a real-time classification system for various alcoholic beverages using machine-learning. The classification performance was enhanced by employing numerous low-cost gas sensors, akin to a dog's olfactory area.

Funder

National Research Foundation of Korea

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/AY/D4AY00964A

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