Comparison of two forensic wood identification technologies for ten Meliaceae woods: computer vision versus mass spectrometry
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/s00226-020-01178-1 http://hdl.handle.net/11449/195489 |
Resumo: | A wealth of forensic wood identification technologies has been developed or improved in recent years, with many attempts to compare results between technologies. The utility of such comparisons is greatly reduced when the species tested with each technology are different and when performance metrics are not calculated or presented in the same way. Here, a species-level XyloTron computer vision model is presented along with a side-by-side comparison for species- and genus-level identification of the 10 species of Meliaceae studied by Deklerck et al. using mass spectrometry. The species-level accuracies of the XyloTron model and the mass spectrometry models are comparable, while the genus-level accuracy of the XyloTron model is higher than that of the mass spectrometry model. The paper concludes with a call for better practices to compare disparate forensic wood identification technologies from a performance driven perspective. |
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Comparison of two forensic wood identification technologies for ten Meliaceae woods: computer vision versus mass spectrometryA wealth of forensic wood identification technologies has been developed or improved in recent years, with many attempts to compare results between technologies. The utility of such comparisons is greatly reduced when the species tested with each technology are different and when performance metrics are not calculated or presented in the same way. Here, a species-level XyloTron computer vision model is presented along with a side-by-side comparison for species- and genus-level identification of the 10 species of Meliaceae studied by Deklerck et al. using mass spectrometry. The species-level accuracies of the XyloTron model and the mass spectrometry models are comparable, while the genus-level accuracy of the XyloTron model is higher than that of the mass spectrometry model. The paper concludes with a call for better practices to compare disparate forensic wood identification technologies from a performance driven perspective.Univ Wisconsin, Dept Bot, Madison, WI 53706 USAUS Forest Serv, Forest Prod Lab, Ctr Wood Anat Res, USDA, Madison, WI 53726 USAPurdue Univ, Dept Forestry & Nat Resources, W Lafayette, IN 47907 USAUniv Estadual Paulista Botucatu, Ciencias Biol Bot, Sao Paulo, BrazilUniv Estadual Paulista Botucatu, Ciencias Biol Bot, Sao Paulo, BrazilSpringerUniv WisconsinUS Forest ServPurdue UnivUniversidade Estadual Paulista (Unesp)Ravindran, PrabuWiedenhoeft, Alex C.2020-12-10T17:36:22Z2020-12-10T17:36:22Z2020-07-04info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1139-1150http://dx.doi.org/10.1007/s00226-020-01178-1Wood Science And Technology. New York: Springer, v. 54, n. 5, p. 1139-1150, 2020.0043-7719http://hdl.handle.net/11449/19548910.1007/s00226-020-01178-1WOS:000545289000001Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengWood Science And Technologyinfo:eu-repo/semantics/openAccess2021-10-23T08:59:40Zoai:repositorio.unesp.br:11449/195489Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:02:55.542615Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Comparison of two forensic wood identification technologies for ten Meliaceae woods: computer vision versus mass spectrometry |
title |
Comparison of two forensic wood identification technologies for ten Meliaceae woods: computer vision versus mass spectrometry |
spellingShingle |
Comparison of two forensic wood identification technologies for ten Meliaceae woods: computer vision versus mass spectrometry Ravindran, Prabu |
title_short |
Comparison of two forensic wood identification technologies for ten Meliaceae woods: computer vision versus mass spectrometry |
title_full |
Comparison of two forensic wood identification technologies for ten Meliaceae woods: computer vision versus mass spectrometry |
title_fullStr |
Comparison of two forensic wood identification technologies for ten Meliaceae woods: computer vision versus mass spectrometry |
title_full_unstemmed |
Comparison of two forensic wood identification technologies for ten Meliaceae woods: computer vision versus mass spectrometry |
title_sort |
Comparison of two forensic wood identification technologies for ten Meliaceae woods: computer vision versus mass spectrometry |
author |
Ravindran, Prabu |
author_facet |
Ravindran, Prabu Wiedenhoeft, Alex C. |
author_role |
author |
author2 |
Wiedenhoeft, Alex C. |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Univ Wisconsin US Forest Serv Purdue Univ Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Ravindran, Prabu Wiedenhoeft, Alex C. |
description |
A wealth of forensic wood identification technologies has been developed or improved in recent years, with many attempts to compare results between technologies. The utility of such comparisons is greatly reduced when the species tested with each technology are different and when performance metrics are not calculated or presented in the same way. Here, a species-level XyloTron computer vision model is presented along with a side-by-side comparison for species- and genus-level identification of the 10 species of Meliaceae studied by Deklerck et al. using mass spectrometry. The species-level accuracies of the XyloTron model and the mass spectrometry models are comparable, while the genus-level accuracy of the XyloTron model is higher than that of the mass spectrometry model. The paper concludes with a call for better practices to compare disparate forensic wood identification technologies from a performance driven perspective. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-10T17:36:22Z 2020-12-10T17:36:22Z 2020-07-04 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1007/s00226-020-01178-1 Wood Science And Technology. New York: Springer, v. 54, n. 5, p. 1139-1150, 2020. 0043-7719 http://hdl.handle.net/11449/195489 10.1007/s00226-020-01178-1 WOS:000545289000001 |
url |
http://dx.doi.org/10.1007/s00226-020-01178-1 http://hdl.handle.net/11449/195489 |
identifier_str_mv |
Wood Science And Technology. New York: Springer, v. 54, n. 5, p. 1139-1150, 2020. 0043-7719 10.1007/s00226-020-01178-1 WOS:000545289000001 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Wood Science And Technology |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
1139-1150 |
dc.publisher.none.fl_str_mv |
Springer |
publisher.none.fl_str_mv |
Springer |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128599475093504 |