Potential field-deployable NIRS identification of seven Dalbergia species listed by CITES

Detalhes bibliográficos
Autor(a) principal: Snel, Filipe A.
Data de Publicação: 2018
Outros Autores: Braga, Jez W. B., Silva, Diego da, Wiedenhoeft, Alex C., Costa, Adriana, Soares, Richard, Coradin, Vera T. R., Pastore, Tereza C. M.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1007/s00226-018-1027-9
http://hdl.handle.net/11449/160491
Resumo: Near-infrared spectroscopy (NIRS) is a potential, field-portable wood identification tool. NIRS has been studied as tool to identify some woods but has not been tested for Dalbergia. This study explored the efficacy of hand-held NIRS technology to discriminate, using multivariate analysis, the spectra of some high-value Dalbergia wood species: D. decipularis, D. sissoo, D. stevensonii, D. latifolia, D. retusa, all of which are listed in CITES Appendix II, and D. nigra, which is listed in CITES Appendix I. Identification models developed using partial least squares discriminant analysis (PLS-DA) and soft independent modeling by class analogy (SIMCA) were compared regarding their ability to answer two sets of identification questions. The first is the identification of each Dalbergia species among the group of the six above, and the second is the separation of D. nigra from a single group comprising the other species, grouping all Dalbergia as one class. For this latter study, spectra of D. cearensis and D. tucurensis were added to the broader Dalbergia class. These spectra were not included in the first set because the number of specimens was not enough to create an exclusive class for them. PLS-DA presented efficiency rates of over 90% in both situations, while SIMCA presented 52% efficiency at species-level separation and 85% efficiency separating D. nigra from other Dalbergia. It was shown that PLS-DA approaches are far better suited than SIMCA for generating a field-deployable NIRS model for discriminating these Dalbergia.
id UNSP_38feca6411d8f744c0a42ab02266e8ee
oai_identifier_str oai:repositorio.unesp.br:11449/160491
network_acronym_str UNSP
network_name_str Repositório Institucional da UNESP
repository_id_str 2946
spelling Potential field-deployable NIRS identification of seven Dalbergia species listed by CITESNear-infrared spectroscopy (NIRS) is a potential, field-portable wood identification tool. NIRS has been studied as tool to identify some woods but has not been tested for Dalbergia. This study explored the efficacy of hand-held NIRS technology to discriminate, using multivariate analysis, the spectra of some high-value Dalbergia wood species: D. decipularis, D. sissoo, D. stevensonii, D. latifolia, D. retusa, all of which are listed in CITES Appendix II, and D. nigra, which is listed in CITES Appendix I. Identification models developed using partial least squares discriminant analysis (PLS-DA) and soft independent modeling by class analogy (SIMCA) were compared regarding their ability to answer two sets of identification questions. The first is the identification of each Dalbergia species among the group of the six above, and the second is the separation of D. nigra from a single group comprising the other species, grouping all Dalbergia as one class. For this latter study, spectra of D. cearensis and D. tucurensis were added to the broader Dalbergia class. These spectra were not included in the first set because the number of specimens was not enough to create an exclusive class for them. PLS-DA presented efficiency rates of over 90% in both situations, while SIMCA presented 52% efficiency at species-level separation and 85% efficiency separating D. nigra from other Dalbergia. It was shown that PLS-DA approaches are far better suited than SIMCA for generating a field-deployable NIRS model for discriminating these Dalbergia.Univ Brasilia, Chem Inst, BR-70910000 Brasilia, DF, BrazilBrazilian Forest Serv, Forest Prod Lab, BR-70818970 Brasilia, DF, BrazilUS Forest Serv, Forest Prod Lab, USDA, Madison, WI 53726 USAUniv Wisconsin, Dept Bot, Madison, WI 53706 USAPurdue Univ, Dept Forestry & Nat Resources, W Lafayette, IN 47907 USAUniv Estadual Paulista, Ciencias Biol Bot, Botucatu, SP, BrazilUniv Estadual Paulista, Ciencias Biol Bot, Botucatu, SP, BrazilSpringerUniversidade de Brasília (UnB)Brazilian Forest ServUS Forest ServUniv WisconsinPurdue UnivUniversidade Estadual Paulista (Unesp)Snel, Filipe A.Braga, Jez W. B.Silva, Diego daWiedenhoeft, Alex C.Costa, AdrianaSoares, RichardCoradin, Vera T. R.Pastore, Tereza C. M.2018-11-26T16:04:42Z2018-11-26T16:04:42Z2018-09-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1411-1427application/pdfhttp://dx.doi.org/10.1007/s00226-018-1027-9Wood Science And Technology. New York: Springer, v. 52, n. 5, p. 1411-1427, 2018.0043-7719http://hdl.handle.net/11449/16049110.1007/s00226-018-1027-9WOS:000441288200015WOS000441288200015.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengWood Science And Technology0,659info:eu-repo/semantics/openAccess2023-11-21T06:10:29Zoai:repositorio.unesp.br:11449/160491Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T18:19:24.406003Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Potential field-deployable NIRS identification of seven Dalbergia species listed by CITES
title Potential field-deployable NIRS identification of seven Dalbergia species listed by CITES
spellingShingle Potential field-deployable NIRS identification of seven Dalbergia species listed by CITES
Snel, Filipe A.
title_short Potential field-deployable NIRS identification of seven Dalbergia species listed by CITES
title_full Potential field-deployable NIRS identification of seven Dalbergia species listed by CITES
title_fullStr Potential field-deployable NIRS identification of seven Dalbergia species listed by CITES
title_full_unstemmed Potential field-deployable NIRS identification of seven Dalbergia species listed by CITES
title_sort Potential field-deployable NIRS identification of seven Dalbergia species listed by CITES
author Snel, Filipe A.
author_facet Snel, Filipe A.
Braga, Jez W. B.
Silva, Diego da
Wiedenhoeft, Alex C.
Costa, Adriana
Soares, Richard
Coradin, Vera T. R.
Pastore, Tereza C. M.
author_role author
author2 Braga, Jez W. B.
Silva, Diego da
Wiedenhoeft, Alex C.
Costa, Adriana
Soares, Richard
Coradin, Vera T. R.
Pastore, Tereza C. M.
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade de Brasília (UnB)
Brazilian Forest Serv
US Forest Serv
Univ Wisconsin
Purdue Univ
Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Snel, Filipe A.
Braga, Jez W. B.
Silva, Diego da
Wiedenhoeft, Alex C.
Costa, Adriana
Soares, Richard
Coradin, Vera T. R.
Pastore, Tereza C. M.
description Near-infrared spectroscopy (NIRS) is a potential, field-portable wood identification tool. NIRS has been studied as tool to identify some woods but has not been tested for Dalbergia. This study explored the efficacy of hand-held NIRS technology to discriminate, using multivariate analysis, the spectra of some high-value Dalbergia wood species: D. decipularis, D. sissoo, D. stevensonii, D. latifolia, D. retusa, all of which are listed in CITES Appendix II, and D. nigra, which is listed in CITES Appendix I. Identification models developed using partial least squares discriminant analysis (PLS-DA) and soft independent modeling by class analogy (SIMCA) were compared regarding their ability to answer two sets of identification questions. The first is the identification of each Dalbergia species among the group of the six above, and the second is the separation of D. nigra from a single group comprising the other species, grouping all Dalbergia as one class. For this latter study, spectra of D. cearensis and D. tucurensis were added to the broader Dalbergia class. These spectra were not included in the first set because the number of specimens was not enough to create an exclusive class for them. PLS-DA presented efficiency rates of over 90% in both situations, while SIMCA presented 52% efficiency at species-level separation and 85% efficiency separating D. nigra from other Dalbergia. It was shown that PLS-DA approaches are far better suited than SIMCA for generating a field-deployable NIRS model for discriminating these Dalbergia.
publishDate 2018
dc.date.none.fl_str_mv 2018-11-26T16:04:42Z
2018-11-26T16:04:42Z
2018-09-01
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/s00226-018-1027-9
Wood Science And Technology. New York: Springer, v. 52, n. 5, p. 1411-1427, 2018.
0043-7719
http://hdl.handle.net/11449/160491
10.1007/s00226-018-1027-9
WOS:000441288200015
WOS000441288200015.pdf
url http://dx.doi.org/10.1007/s00226-018-1027-9
http://hdl.handle.net/11449/160491
identifier_str_mv Wood Science And Technology. New York: Springer, v. 52, n. 5, p. 1411-1427, 2018.
0043-7719
10.1007/s00226-018-1027-9
WOS:000441288200015
WOS000441288200015.pdf
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Wood Science And Technology
0,659
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 1411-1427
application/pdf
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_ 1808128919448059904