Abstract
This paper presents the application of the backward feature elimination technique on an electronic nose (E-nose) to aid the rapid detection of pathogens using Volatile Organic Compounds (VOCs). The timely identification of pathogens is vital to facilitate control of diseases. E-noses are widely used for the identification of VOCs as a non-invasive tool. However, the identification of VOC signatures associated with microbial pathogens using E-nose is currently inefficient for the timely identification of pathogens. Therefore, we proposed an E-nose system integrating the backward feature elimination. Comprehensive experiments of backward feature elimination showed that they improve the classification accuracy.
Original language | English |
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Title of host publication | 2020 IEEE International Conference on Consumer Electronics, ICCE 2020 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781728151861 |
DOIs | |
Publication status | Published - Jan 2020 |
Externally published | Yes |
Event | 2020 IEEE International Conference on Consumer Electronics, ICCE 2020 - Las Vegas, United States Duration: 4 Jan 2020 → 6 Jan 2020 |
Publication series
Name | Digest of Technical Papers - IEEE International Conference on Consumer Electronics |
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Volume | 2020-January |
ISSN (Print) | 0747-668X |
Conference
Conference | 2020 IEEE International Conference on Consumer Electronics, ICCE 2020 |
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Country/Territory | United States |
City | Las Vegas |
Period | 4/01/20 → 6/01/20 |
Bibliographical note
Publisher Copyright:© 2020 IEEE.
Funding
We would like to thank Susana Palma, Ana Traguedo, Ana Porteira, Maria Frias, Hugo Gamboa, and Ana Roque for making their comprehensive dataset available for this research. This project was jointly supported by Zoetis through the vHive initiative and LMDP project through UK BBSRC funding(Grant reference number BB/R012695/1).
Funders | Funder number |
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Zoetis | |
Biotechnology and Biological Sciences Research Council | BB/R012695/1 |
Keywords
- Backward feature elimination
- Electronic nose
- Machine learning
- Pathogen detection