A practical study of CITES wood species identification by untargeted DART/QTOF, GC/QTOF and LC/QTOF together with machine learning processes and statistical analysis
Illegal logging and trafficking of endangered timber species has attracted the world's major organized crime groups, with associated deforestation and serious social damage. The inability of traditional methodologies and DNA analysis to readily perform wood identification to the species level f...
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doaj-34cb0ce6d3f4439386e5edc2fee432992021-10-01T05:13:13ZengElsevierEnvironmental Advances2666-76572021-10-015100089A practical study of CITES wood species identification by untargeted DART/QTOF, GC/QTOF and LC/QTOF together with machine learning processes and statistical analysisPamela Brunswick0Daniel Cuthbertson1Jeffrey Yan2Candice C. Chua3Isabelle Duchesne4Nathalie Isabel5Philip D. Evans6Peter Gasson7Geoffrey Kite8Joy Bruno9Graham van Aggelen10Dayue Shang11Pacific and Yukon Laboratory for Environmental Testing (PYLET), Science & Technology Branch, Environment and Climate Change Canada, North Vancouver, British Columbia, Canada; Corresponding authors.Agilent Technologies Inc., Santa Clara, California, United StatesPacific and Yukon Laboratory for Environmental Testing (PYLET), Science & Technology Branch, Environment and Climate Change Canada, North Vancouver, British Columbia, CanadaPacific and Yukon Laboratory for Environmental Testing (PYLET), Science & Technology Branch, Environment and Climate Change Canada, North Vancouver, British Columbia, CanadaCanadian Wood Fibre Centre, Canadian Forest Service, Natural Resources Canada, Quebec, CanadaCanadian Wood Fibre Centre, Canadian Forest Service, Natural Resources Canada, Quebec, CanadaFaculty of Forestry, Department of Wood Science, University of British Columbia, Vancouver, BC, CanadaRoyal Botanic Gardens, Kew, Richmond, Surrey, United KingdomRoyal Botanic Gardens, Kew, Richmond, Surrey, United KingdomPacific and Yukon Laboratory for Environmental Testing (PYLET), Science & Technology Branch, Environment and Climate Change Canada, North Vancouver, British Columbia, CanadaPacific and Yukon Laboratory for Environmental Testing (PYLET), Science & Technology Branch, Environment and Climate Change Canada, North Vancouver, British Columbia, CanadaPacific and Yukon Laboratory for Environmental Testing (PYLET), Science & Technology Branch, Environment and Climate Change Canada, North Vancouver, British Columbia, Canada; Corresponding authors.Illegal logging and trafficking of endangered timber species has attracted the world's major organized crime groups, with associated deforestation and serious social damage. The inability of traditional methodologies and DNA analysis to readily perform wood identification to the species level for monitoring has stimulated research on chemotyping techniques. In this study, simple wood extraction of endangered rosewoods (Dalbergia spp), amenable to use in the field, produced colorful hues that were suggestive of wood species. A more definitive study was conducted to develop wood species identification procedures using high-resolution quadrupole time-of-flight (QTOF) mass spectrometers interfaced with liquid chromatography (LC), gas chromatography (GC), and Direct Analysis in Real Time (DART). The time consuming process of extracting “identifying” mass spectral ions for species identification, contentious due to their ubiquitous nature, was supplanted by application of machine learning processes. The unbiased software mining of raw data from multiple analytical batches, followed by statistical Random Forest analysis, enabled discrimination between both anatomically and chemotypically similar Dalbergia species. Statistical Principal Component Analysis (PCA) scatterplots with 95% confidence ellipses were visually compelling in showing a differential clustering of Dalbergia from other commonly traded and lookalike wood species. The information rich raw data from GC or LC analyses offered a corroborative, legally defensible, and widely available confirmatory tool in the identification of timber species.http://www.sciencedirect.com/science/article/pii/S2666765721000600DARTQTOFDalbergiaSpeciesIdentificationCITES |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Pamela Brunswick Daniel Cuthbertson Jeffrey Yan Candice C. Chua Isabelle Duchesne Nathalie Isabel Philip D. Evans Peter Gasson Geoffrey Kite Joy Bruno Graham van Aggelen Dayue Shang |
spellingShingle |
Pamela Brunswick Daniel Cuthbertson Jeffrey Yan Candice C. Chua Isabelle Duchesne Nathalie Isabel Philip D. Evans Peter Gasson Geoffrey Kite Joy Bruno Graham van Aggelen Dayue Shang A practical study of CITES wood species identification by untargeted DART/QTOF, GC/QTOF and LC/QTOF together with machine learning processes and statistical analysis Environmental Advances DART QTOF Dalbergia Species Identification CITES |
author_facet |
Pamela Brunswick Daniel Cuthbertson Jeffrey Yan Candice C. Chua Isabelle Duchesne Nathalie Isabel Philip D. Evans Peter Gasson Geoffrey Kite Joy Bruno Graham van Aggelen Dayue Shang |
author_sort |
Pamela Brunswick |
title |
A practical study of CITES wood species identification by untargeted DART/QTOF, GC/QTOF and LC/QTOF together with machine learning processes and statistical analysis |
title_short |
A practical study of CITES wood species identification by untargeted DART/QTOF, GC/QTOF and LC/QTOF together with machine learning processes and statistical analysis |
title_full |
A practical study of CITES wood species identification by untargeted DART/QTOF, GC/QTOF and LC/QTOF together with machine learning processes and statistical analysis |
title_fullStr |
A practical study of CITES wood species identification by untargeted DART/QTOF, GC/QTOF and LC/QTOF together with machine learning processes and statistical analysis |
title_full_unstemmed |
A practical study of CITES wood species identification by untargeted DART/QTOF, GC/QTOF and LC/QTOF together with machine learning processes and statistical analysis |
title_sort |
practical study of cites wood species identification by untargeted dart/qtof, gc/qtof and lc/qtof together with machine learning processes and statistical analysis |
publisher |
Elsevier |
series |
Environmental Advances |
issn |
2666-7657 |
publishDate |
2021-10-01 |
description |
Illegal logging and trafficking of endangered timber species has attracted the world's major organized crime groups, with associated deforestation and serious social damage. The inability of traditional methodologies and DNA analysis to readily perform wood identification to the species level for monitoring has stimulated research on chemotyping techniques. In this study, simple wood extraction of endangered rosewoods (Dalbergia spp), amenable to use in the field, produced colorful hues that were suggestive of wood species. A more definitive study was conducted to develop wood species identification procedures using high-resolution quadrupole time-of-flight (QTOF) mass spectrometers interfaced with liquid chromatography (LC), gas chromatography (GC), and Direct Analysis in Real Time (DART). The time consuming process of extracting “identifying” mass spectral ions for species identification, contentious due to their ubiquitous nature, was supplanted by application of machine learning processes. The unbiased software mining of raw data from multiple analytical batches, followed by statistical Random Forest analysis, enabled discrimination between both anatomically and chemotypically similar Dalbergia species. Statistical Principal Component Analysis (PCA) scatterplots with 95% confidence ellipses were visually compelling in showing a differential clustering of Dalbergia from other commonly traded and lookalike wood species. The information rich raw data from GC or LC analyses offered a corroborative, legally defensible, and widely available confirmatory tool in the identification of timber species. |
topic |
DART QTOF Dalbergia Species Identification CITES |
url |
http://www.sciencedirect.com/science/article/pii/S2666765721000600 |
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