Automatic detection of oestrus cows via breath sampling with an electronic nose: A pilot study

Francis E.P. Sanderink; Jan Willem Gerritsen; Peter W.G. Groot Koerkamp; Simon van Mourik

doi:10.1016/j.biosystemseng.2017.01.004

Automatic detection of oestrus cows via breath sampling with an electronic nose: A pilot study

Francis E.P. Sanderink, Jan Willem Gerritsen, Peter W.G. Groot Koerkamp, Simon van Mourik^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

4 Citations (Scopus)

Abstract

To explore the possibility of automating the process of detecting oestrus cows via electronic olfaction, we designed and built an integrated measurement system consisting of a breath sample device, electronic nose, and a diagnosis model. The diagnosis performance, using a data set of 71 measurements on 52 cows in total, was a receiver operating characteristic curve with an area under the curve of 0.86, which had 83% sensitivity and 86% specificity for a specifically chosen threshold value. These results indicate a potential for automated detection based on breath sampling.

Original language	English
Pages (from-to)	1-6
Journal	Biosystems Engineering
Volume	156
DOIs	https://doi.org/10.1016/j.biosystemseng.2017.01.004
Publication status	Published - 2017

Keywords

Diagnosis
Electronic nose (eNose)
Exhaled breath
Oestrus

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Sanderink, F. E. P., Gerritsen, J. W., Groot Koerkamp, P. W. G., & van Mourik, S. (2017). Automatic detection of oestrus cows via breath sampling with an electronic nose: A pilot study. Biosystems Engineering, 156, 1-6. https://doi.org/10.1016/j.biosystemseng.2017.01.004

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abstract = "To explore the possibility of automating the process of detecting oestrus cows via electronic olfaction, we designed and built an integrated measurement system consisting of a breath sample device, electronic nose, and a diagnosis model. The diagnosis performance, using a data set of 71 measurements on 52 cows in total, was a receiver operating characteristic curve with an area under the curve of 0.86, which had 83{\%} sensitivity and 86{\%} specificity for a specifically chosen threshold value. These results indicate a potential for automated detection based on breath sampling.",

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