Detection of Emerald Ash Borer Infestations in Living Green Ash by Noninvasive Electronic-Nose Analysis of Wood Volatiles
The emerald ash borer (EAB) has been the most destructive and costly nonnative insect to threaten the health of ash (<i>Fraxinus</i>) species in North America for at least the past 25 years. The development of methods for detecting visually-hidden EAB galleries at early stages of infesta...
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doaj-c0cb8c226f244df1bb56110b20344be12020-11-25T01:39:23ZengMDPI AGBiosensors2079-63742019-10-019412310.3390/bios9040123bios9040123Detection of Emerald Ash Borer Infestations in Living Green Ash by Noninvasive Electronic-Nose Analysis of Wood VolatilesA. Dan Wilson0Lisa B. Forse1Benjamin A. Babst2Mohammad M. Bataineh3Pathology Department, Southern Hardwoods Laboratory, Southern Research Station, USDA Forest Service, Stoneville, MS 38776, USAPathology Department, Southern Hardwoods Laboratory, Southern Research Station, USDA Forest Service, Stoneville, MS 38776, USAArkansas Forest Resources Center, and College of Forestry, Agriculture and Natural Resources, University of Arkansas at Monticello, Monticello, AR 71656, USAArkansas Forest Resources Center, and College of Forestry, Agriculture and Natural Resources, University of Arkansas at Monticello, Monticello, AR 71656, USAThe emerald ash borer (EAB) has been the most destructive and costly nonnative insect to threaten the health of ash (<i>Fraxinus</i>) species in North America for at least the past 25 years. The development of methods for detecting visually-hidden EAB galleries at early stages of infestation would provide a useful tool to more effectively facilitate the planning and implementation of targeted EAB pest-suppression and management activities. We tested the efficacy of using a dual-technology electronic-nose (e-nose)/gas chromatograph device as a means for detection of EAB infestations in green ash trees in different EAB-decline classes by analysis of VOC emissions in sapwood. We found significant differences in VOC profiles for trees from the four decline classes. The VOC composition, quantities, and types of volatile metabolites present in headspace volatiles varied considerably across sample types, and resulted in distinct e-nose smellprint patterns that were characteristic of each unique chemical composition. In addition, specific VOC metabolites were identified as potential healthy and EAB-infestation biomarkers, indicative of the health states of individual trees. Few significant differences in major bark phenolic compounds were found between ash decline classes using LC-MS. The e-nose was effective in discriminating between uninfested and EAB-infested trees based on sapwood VOC emissions.https://www.mdpi.com/2079-6374/9/4/123<i>agrilus planipennis</i>early tree-infestation detectionelectronic nose (e-nose)plant-health biomarkersinsect-infestation biomarkerssapwoodsmellprint signaturesvoc-metabolites |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
A. Dan Wilson Lisa B. Forse Benjamin A. Babst Mohammad M. Bataineh |
spellingShingle |
A. Dan Wilson Lisa B. Forse Benjamin A. Babst Mohammad M. Bataineh Detection of Emerald Ash Borer Infestations in Living Green Ash by Noninvasive Electronic-Nose Analysis of Wood Volatiles Biosensors <i>agrilus planipennis</i> early tree-infestation detection electronic nose (e-nose) plant-health biomarkers insect-infestation biomarkers sapwood smellprint signatures voc-metabolites |
author_facet |
A. Dan Wilson Lisa B. Forse Benjamin A. Babst Mohammad M. Bataineh |
author_sort |
A. Dan Wilson |
title |
Detection of Emerald Ash Borer Infestations in Living Green Ash by Noninvasive Electronic-Nose Analysis of Wood Volatiles |
title_short |
Detection of Emerald Ash Borer Infestations in Living Green Ash by Noninvasive Electronic-Nose Analysis of Wood Volatiles |
title_full |
Detection of Emerald Ash Borer Infestations in Living Green Ash by Noninvasive Electronic-Nose Analysis of Wood Volatiles |
title_fullStr |
Detection of Emerald Ash Borer Infestations in Living Green Ash by Noninvasive Electronic-Nose Analysis of Wood Volatiles |
title_full_unstemmed |
Detection of Emerald Ash Borer Infestations in Living Green Ash by Noninvasive Electronic-Nose Analysis of Wood Volatiles |
title_sort |
detection of emerald ash borer infestations in living green ash by noninvasive electronic-nose analysis of wood volatiles |
publisher |
MDPI AG |
series |
Biosensors |
issn |
2079-6374 |
publishDate |
2019-10-01 |
description |
The emerald ash borer (EAB) has been the most destructive and costly nonnative insect to threaten the health of ash (<i>Fraxinus</i>) species in North America for at least the past 25 years. The development of methods for detecting visually-hidden EAB galleries at early stages of infestation would provide a useful tool to more effectively facilitate the planning and implementation of targeted EAB pest-suppression and management activities. We tested the efficacy of using a dual-technology electronic-nose (e-nose)/gas chromatograph device as a means for detection of EAB infestations in green ash trees in different EAB-decline classes by analysis of VOC emissions in sapwood. We found significant differences in VOC profiles for trees from the four decline classes. The VOC composition, quantities, and types of volatile metabolites present in headspace volatiles varied considerably across sample types, and resulted in distinct e-nose smellprint patterns that were characteristic of each unique chemical composition. In addition, specific VOC metabolites were identified as potential healthy and EAB-infestation biomarkers, indicative of the health states of individual trees. Few significant differences in major bark phenolic compounds were found between ash decline classes using LC-MS. The e-nose was effective in discriminating between uninfested and EAB-infested trees based on sapwood VOC emissions. |
topic |
<i>agrilus planipennis</i> early tree-infestation detection electronic nose (e-nose) plant-health biomarkers insect-infestation biomarkers sapwood smellprint signatures voc-metabolites |
url |
https://www.mdpi.com/2079-6374/9/4/123 |
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