People stink! Towards identification of people from breath samples

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Abstract

The paper addresses the potential to use breath samples for identifying people. Participants were asked to exhale ten times for a length of five seconds to a tube attached to a commercial ion-mobility spectrometry device on three separate sessions. The data of each participant was divided into training (50% of the samples) and test data sets (50% of the samples) in random order. Classification decision tree (CDT), K nearest neighbor (KNN), naïve Bayes (NB), linear discriminant analysis (LDA), and quadratic discriminant analysis (QDA) were used to analyze if the data could be classified correctly. Within a session, KNN (75.2%), NB (78.3%), and LDA (85.8%) were able to identify participants. Between sessions, the performance decreased.
Original languageEnglish
Title of host publication2022 International Symposium on Olfaction and Electronic Nose (ISOEN)
PublisherIEEE
Pages1-3
ISBN (Electronic)978-1-6654-5860-3
DOIs
Publication statusPublished - Jun 2022
Publication typeA4 Article in conference proceedings
EventIEEE International Symposium on Olfaction and Electronic Nose (ISOEN) - Aveiro, Portugal
Duration: 29 May 20221 Jun 2022

Conference

ConferenceIEEE International Symposium on Olfaction and Electronic Nose (ISOEN)
Country/TerritoryPortugal
CityAveiro
Period29/05/221/06/22

Publication forum classification

  • Publication forum level 1

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