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An automated approach to detecting signals in electroantennogram data

Journal of Chemical Ecology

By:
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DOI: 10.1007/s10886-007-9338-6

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Abstract

Coupled gas chromatography/electroantennographic detection (GC-EAD) is a widely used method for identifying insect olfactory stimulants present in mixtures of volatiles, and it can greatly accelerate the identification of insect semiochemicals. In GC-EAD, voltage changes across an insect's antenna are measured while the antenna is exposed to compounds eluting from a gas chromatograph. The antenna thus serves as a selective GC detector whose output can be compared to that of a "general" GC detector, commonly a flame ionization detector. Appropriate interpretation of GC-EAD results requires that olfaction-related voltage changes in the antenna be distinguishable from background noise that arises inevitably from antennal preparations and the GC-EAD-associated hardware. In this paper, we describe and compare mathematical algorithms for discriminating olfaction-generated signals in an EAD trace from background noise. The algorithms amplify signals by recognizing their characteristic shape and wavelength while suppressing unstructured noise. We have found these algorithms to be both powerful and highly discriminatory even when applied to noisy traces where the signals would be difficult to discriminate by eye. This new methodology removes operator bias as a factor in signal identification, can improve realized sensitivity of the EAD system, and reduces the number of runs required to confirm the identity of an olfactory stimulant. ?? 2007 Springer Science+Business Media, LLC.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
An automated approach to detecting signals in electroantennogram data
Series title:
Journal of Chemical Ecology
DOI:
10.1007/s10886-007-9338-6
Volume
33
Issue:
9
Year Published:
2007
Language:
English
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
Journal of Chemical Ecology
First page:
1748
Last page:
1762
Number of Pages:
15