文献类型：期刊文献

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

作者：He, Tuo[1,2] Jiao, Lichao[1,2] Yu, Min[1,2] Guo, Juan[1,2] Jiang, Xiaomei[1,2] Yin, Yafang[1,2]

第一作者：何拓

通信作者：Yin, YF[1];Yin, YF[2]

机构：[1]Chinese Acad Forestry, Chinese Res Inst Wood Ind, Dept Wood Anat & Utilizat, Beijing 100091, Peoples R China;[2]Chinese Acad Forestry, Wood Collect WOODPEDIA, Beijing 100091, Peoples R China

年份：2019

卷号：73

期号：3

起止页码：277-285

外文期刊名：HOLZFORSCHUNG

收录：;EI(收录号：20184105917445);Scopus(收录号：2-s2.0-85054364824);WOS:【SCI-EXPANDED(收录号:WOS:000460124200009)】；

基金：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).

语种：英文

外文关键词：Dalbergia timber species; DNA barcoding; illegal logging; machine learning approaches (MIAs); reference library; SMO classifier; wood identification

摘要：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%similar to 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 combination with DNA barcode reference libraries for the identification of endangered Dalbergia timber species.