Backward feature elimination for accurate pathogen recognition using portable electronic nose

Mukunthan Tharmakulasingam, Cihan Topal, Anil Fernando, Roberto La Ragione

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Citations (Scopus)

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 languageEnglish
Title of host publication2020 IEEE International Conference on Consumer Electronics, ICCE 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728151861
DOIs
Publication statusPublished - Jan 2020
Externally publishedYes
Event2020 IEEE International Conference on Consumer Electronics, ICCE 2020 - Las Vegas, United States
Duration: 4 Jan 20206 Jan 2020

Publication series

NameDigest of Technical Papers - IEEE International Conference on Consumer Electronics
Volume2020-January
ISSN (Print)0747-668X

Conference

Conference2020 IEEE International Conference on Consumer Electronics, ICCE 2020
Country/TerritoryUnited States
CityLas Vegas
Period4/01/206/01/20

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).

FundersFunder number
Zoetis
Biotechnology and Biological Sciences Research CouncilBB/R012695/1

    Keywords

    • Backward feature elimination
    • Electronic nose
    • Machine learning
    • Pathogen detection

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