Can quantitative wood anatomy data coupled with machine learning analysis discriminate CITES species from their look-alikes?

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorChinese Academy of Forestry-
Autor(es): dc.contributorUSDA Forest Service-
Autor(es): dc.contributorUniversity of Wisconsin-
Autor(es): dc.contributorPurdue University-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorMississippi State University-
Autor(es): dc.creatorLiu, Shoujia-
Autor(es): dc.creatorHe, Tuo-
Autor(es): dc.creatorWang, Jiajun-
Autor(es): dc.creatorChen, Jiabao-
Autor(es): dc.creatorGuo, Juan-
Autor(es): dc.creatorJiang, Xiaomei-
Autor(es): dc.creatorWiedenhoeft, Alex C.-
Autor(es): dc.creatorYin, Yafang-
Data de aceite: dc.date.accessioned2025-08-21T18:53:10Z-
Data de disponibilização: dc.date.available2025-08-21T18:53:10Z-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2021-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/s00226-022-01404-y-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/240628-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/240628-
Descrição: dc.descriptionDue to increasing global trade of timber commodities and illegal logging activities, wood species listed in the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) appendices are facing extinction, and their international trade has been banned or is under supervision. Reliable and applicable species-level discrimination methods have become urgent to protect global forest resources and promote the legal trade of timbers. This study aims to discriminate CITES-listed species from their look-alikes in international trade using quantitative wood anatomy (QWA) data coupled with machine learning (ML) analysis. Herein, the QWA data of 14 CITES-listed species and 15 of their look-alike species were collected from microscope slide collection, and four ML classifiers, J48, Multinomial Naïve Bayes, Random Forest, and SMO, were used to analyze the QWA data. The results indicated that ML classifiers exhibited better performance than traditional wood identification methods. Specifically, Multinomial Naïve Bayes outperformed other classifiers, and successfully discriminated CITES-listed Pterocarpus species from their look-alike species with an accuracy of 95.83%. Furthermore, the discrimination accuracy was affected by the combinations of wood anatomical features, and combinations with fewer features included could result in higher accuracy at the species level. In conclusion, the QWA data coupled with ML analysis could unlock the potential of wood anatomy to discriminate CITES species from their look-alikes for forensic applications.-
Descrição: dc.descriptionDepartment of Wood Anatomy and Utilization Research Institute of Wood Industry Chinese Academy of Forestry-
Descrição: dc.descriptionWood Collections Chinese Academy of Forestry-
Descrição: dc.descriptionCenter for Wood Anatomy Research Forest Products Laboratory USDA Forest Service-
Descrição: dc.descriptionDepartment of Botany University of Wisconsin-
Descrição: dc.descriptionDepartment of Forestry and National Resources Purdue University-
Descrição: dc.descriptionCiências Biológicas (Botânica) Universidade Estadual Paulista, São Paulo-
Descrição: dc.descriptionDepartment of Sustainable Biomaterials Mississippi State University-
Descrição: dc.descriptionCiências Biológicas (Botânica) Universidade Estadual Paulista, São Paulo-
Idioma: dc.languageen-
Relação: dc.relationWood Science and Technology-
???dc.source???: dc.sourceScopus-
Título: dc.titleCan quantitative wood anatomy data coupled with machine learning analysis discriminate CITES species from their look-alikes?-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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