Bayesian approaches to assigning the source of an odour detected by an electronic nose

D. Brynn Hibbert

doi:10.1071/CH24053

RESEARCH ARTICLE (Open Access)

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Bayesian approaches to assigning the source of an odour detected by an electronic nose

D. Brynn Hibbert

^A ^*

+ Author Affiliations

- Author Affiliations

^A School of Chemistry, UNSW Sydney, Sydney, NSW 2052, Australia.

^* Correspondence to: b.hibbert@unsw.edu.au

Handling Editor: Curt Wentrup

Australian Journal of Chemistry 77, CH24053 https://doi.org/10.1071/CH24053

Submitted: 2 May 2024 Accepted: 28 August 2024 Published online: 18 September 2024

© 2024 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

After a brief review of electronic nose technology, the use of an Australian electronic nose to identify an unknown odour out of a set of known odours is described. Multivariate supervised learning is accomplished by applying Bayes’ theorem to data from metal oxide semiconductor sensors responding to each of a number of target odours. An odour from an unknown source is then assigned a probability of membership of each of the training sets by applying either a Naïve Bayes algorithm to the deemed independent data from each sensor, or to a multinormal distribution of the data. A flat prior (equal probabilities of each outcome) is usually adopted, but for particular situations where one odour is known to predominate, then suitably weighted priors can be used. A source ‘none of the above’, which has a small likelihood covering the space of the possible sensor responses, is included for completeness. This also avoids the assignment to a source that has an extremely small probability but which is greater than that of any other source. Examples are given of a single source (detecting diabetes from a patient’s breath), and three sources of unpleasant odours in a meat processing plant.

Keywords: Bayes theorem, diabetes diagnosis, electronic nose, environmental monitoring, meat processing, MOS sensors, Naïve Bayes, odour recognition.

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