Online Scent Classification by Ion-Mobility Spectrometry Sequences

Philipp Müller, Katri Salminen, Anton Kontunen, Markus Karjalainen, Poika Isokoski, Jussi Rantala, Joni Leivo, Jari Väliaho, Pasi Kallio, Jukka Lekkala, Veikko Surakkka

Tutkimustuotos: ArtikkeliScientificvertaisarvioitu

2 Sitaatiot (Scopus)
36 Lataukset (Pure)


For ion-mobility spectrometry (IMS)-based electronic noses (eNose) samples of scents are markedly time-dependent, with a transient phase and a highly volatile stable phase in certain conditions. At the same time, the samples depend on various environmental factors, such as temperature and humidity. This makes fast classification of scents challenging. The present aim was to develop and test an algorithm for online scent classification that mitigates these dependencies by using both baseline measurements and sequences of samples for classification. A classifier based on the K nearest neighbors approach was derived. The classifier is able to use measurements from both transient and stable phase, yields a label for the analyzed scent, and information on the trustworthiness of the returned label. In order to avoid the classifier being fooled by irrelevant features and to reduce the dimensionality of the feature space, principal component analysis was applied to the data. The classifier was tested with four food scents, each presented in two different ways to the IMS. By using baseline measurements, the misclassification rate was reduced from 20.0 to 13.3%. A second experiment showed that the used IMS type experiences device heterogeneity.
JulkaisuFrontiers in Applied Mathematics and Statistics
DOI - pysyväislinkit
TilaJulkaistu - 2019
OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä


  • K nearest neighbors
  • device heterogeneity
  • electronic nose
  • ion-mobility spectrometry
  • scent classification
  • time-series analysis


  • Jufo-taso 1


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