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Wood Research and Technology

Holzforschung

Cellulose – Hemicelluloses – Lignin – Wood Extractives

Editor-in-Chief: Salmén, Lennart

Editorial Board: Daniel, Geoffrey / Militz, Holger / Rosenau, Thomas / Sixta, Herbert / Vuorinen, Tapani / Argyropoulos, Dimitris S. / Balakshin, Yu / Barnett, J. R. / Burgert, Ingo / Rio, Jose C. / Evans, Robert / Evtuguin, Dmitry V. / Frazier, Charles E. / Fukushima, Kazuhiko / Gindl-Altmutter, Wolfgang / Glasser, W. G. / Holmbom, Bjarne / Isogai, Akira / Kadla, John F. / Koch, Gerald / Lachenal, Dominique / Laine, Christiane / Mansfield, Shawn D. / Morrell, J.J. / Niemz, Peter / Potthast, Antje / Ragauskas, Arthur J. / Ralph, John / Rice, Robert W. / Salin, Jarl-Gunnar / Schmitt, Uwe / Schultz, Tor P. / Sipilä, Jussi / Takano, Toshiyuki / Tamminen, Tarja / Theliander, Hans / Welling, Johannes / Willför, Stefan / Yoshihara, Hiroshi


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Volume 73, Issue 3

Issues

DNA barcoding authentication for the wood of eight endangered Dalbergia timber species using machine learning approaches

Tuo He
  • Department of Wood Anatomy and Utilization, Chinese Research Institute of Wood Industry, Chinese Academy of Forestry, Beijing 100091, China
  • Wood Collections (WOODPEDIA), Chinese Academy of Forestry, Beijing 100091, China
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Lichao Jiao
  • Department of Wood Anatomy and Utilization, Chinese Research Institute of Wood Industry, Chinese Academy of Forestry, Beijing 100091, China
  • Wood Collections (WOODPEDIA), Chinese Academy of Forestry, Beijing 100091, China
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Min Yu
  • Department of Wood Anatomy and Utilization, Chinese Research Institute of Wood Industry, Chinese Academy of Forestry, Beijing 100091, China
  • Wood Collections (WOODPEDIA), Chinese Academy of Forestry, Beijing 100091, China
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Juan Guo
  • Department of Wood Anatomy and Utilization, Chinese Research Institute of Wood Industry, Chinese Academy of Forestry, Beijing 100091, China
  • Wood Collections (WOODPEDIA), Chinese Academy of Forestry, Beijing 100091, China
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Xiaomei Jiang
  • Department of Wood Anatomy and Utilization, Chinese Research Institute of Wood Industry, Chinese Academy of Forestry, Beijing 100091, China
  • Wood Collections (WOODPEDIA), Chinese Academy of Forestry, Beijing 100091, China
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Yafang Yin
  • Corresponding author
  • Department of Wood Anatomy and Utilization, Chinese Research Institute of Wood Industry, Chinese Academy of Forestry, Beijing 100091, China
  • Wood Collections (WOODPEDIA), Chinese Academy of Forestry, Beijing 100091, China, Phone: +86 10 6288 9468, Fax: +86 10 6288 1937
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2018-09-18 | DOI: https://doi.org/10.1515/hf-2018-0076

Abstract

Reliable wood identification and proof of the provenance of trees is the first step for combating illegal logging. DNA barcoding belongs to the promising tools in this regard, for which reliable methods and reference libraries are needed. Machine learning approaches (MLAs) are tailored to the necessities of DNA barcoding, which are based on mathematical multivaried analysis. In the present study, eight Dalbergia timber species were investigated in terms of their DNA sequences focusing on four barcodes (ITS2, matK, trnH-psbA and trnL) by means of the MLAs BLOG and WEKA for wood species identification. The data material downloaded from NCBI (288 sequences) and taken from a previous study of the authors (153 DNA sequences) was taken as dataset for calibration. The MLAs’ effectivity was verified through identification of non-vouchered wood specimens. The results indicate that the SMO classifier as part of the WEKA approach performed the best (98%~100%) for discriminating the eight Dalbergia timber species. Moreover, the two-locus combination ITS2+trnH-psbA showed the highest success rate. Furthermore, the non-vouchered wood specimens were successfully identified by means of ITS2+trnH-psbA with the SMO classifier. The MLAs are successful in combi- nation with DNA barcode reference libraries for the identification of endangered Dalbergia timber species.

This article offers supplementary material which is provided at the end of the article.

Keywords: Dalbergia timber species; DNA barcoding; illegal logging; machine learning approaches (MLAs); reference library; SMO classifier; wood identification

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About the article

Received: 2018-04-06

Accepted: 2018-08-15

Published Online: 2018-09-18

Published in Print: 2019-03-26


Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

Research funding: This work was financially supported by National Natural Science Foundation of China, Funder Id: 10.13039/501100001809 (Grant No. 31600451), the Fundamental Research Funds of Chinese Academy of Forestry, Funder Id: 10.13039/501100004543 (Grant No. CAFYBB2017ZE003), and the China Scholarship Council (Grant No. 2017-3109).

Employment or leadership: None declared.

Honorarium: None declared.


Citation Information: Holzforschung, Volume 73, Issue 3, Pages 277–285, ISSN (Online) 1437-434X, ISSN (Print) 0018-3830, DOI: https://doi.org/10.1515/hf-2018-0076.

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