Characterisation of humidity dependence of a metal oxide semiconductor sensor array using partial least squares

Article

Sohn, Jae Ho, Atzeni, Michael, Zeller, Les and Pioggia, Giovanni. 2008. "Characterisation of humidity dependence of a metal oxide semiconductor sensor array using partial least squares." Sensors and Actuators B: Chemical: international journal devoted to research and development of physical and chemical transducers. 131 (1), pp. 230-235. https://doi.org/10.1016/j.snb.2007.11.009
Article Title

Characterisation of humidity dependence of a metal oxide semiconductor sensor array using partial least squares

ERA Journal ID1500
Article CategoryArticle
AuthorsSohn, Jae Ho (Author), Atzeni, Michael (Author), Zeller, Les (Author) and Pioggia, Giovanni (Author)
Journal TitleSensors and Actuators B: Chemical: international journal devoted to research and development of physical and chemical transducers
Journal Citation131 (1), pp. 230-235
Number of Pages6
Year2008
Place of PublicationUnited States
ISSN0925-4005
1873-3077
Digital Object Identifier (DOI)https://doi.org/10.1016/j.snb.2007.11.009
Abstract

[Abstract]:
Metal oxide semiconductor (MOS) sensors are a class of chemical sensors that have potential for being a practical core sensor module for an electronic nose system in various environmental monitoring applications. However, the responses of these sensors may be affected by changes in humidity and this must be taken into consideration when developing calibration models. This paper characterises the humidity dependence of a sensor array which consists of 12 MOS sensors. The results were used to develop calibration models using partial least squares (PLS). Effects of humidity on the response of the sensor array and predictive ability of partial least squares are discussed. It is shown that partial least squares can
provide proper calibration models to compensate for effects caused by changes in humidity.

Keywordspartial least squares; metal oxide semiconductor sensor; calibration; electronic nose
ANZSRC Field of Research 2020300207. Agricultural systems analysis and modelling
401102. Environmentally sustainable engineering
300101. Agricultural biotechnology diagnostics (incl. biosensors)
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Byline AffiliationsDepartment of Primary Industries and Fisheries, Queensland
University of Pisa, Italy
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