文献类型：期刊文献

英文题名：Similarity network fusion for aggregating headspace GC-MS and direct analysis in real time-mass spectrometry data from solid samples to enhance species identification efficiency of high-temperature heated wood

作者：Zhang, Maomao[1,2,3] Guo, Juan[2,3] Lu, Yang[2,3] Jiao, Lichao[2,3] He, Tuo[2,3] Yin, Yafang[2,3]

第一作者：Zhang, Maomao

通信作者：Yin, YF[1];Yin, YF[2]

机构：[1]Xian Univ Architecture & Technol, Coll Art, Xian 710055, Peoples R China;[2]Chinese Acad Forestry, Chinese Res Inst Wood Ind, Dept Wood Anat & Utilizat, Beijing 100091, Peoples R China;[3]Chinese Acad Forestry, Wood Collect WOODPEDIA, Beijing 100091, Peoples R China

年份：2022

卷号：68

期号：1

外文期刊名：JOURNAL OF WOOD SCIENCE

收录：;EI(收录号：20222612279408);Scopus(收录号：2-s2.0-85132693072);WOS:【SCI-EXPANDED(收录号:WOS:000814246600001)】；

基金：This work was supported financially by the Project of Shaanxi Provincial Science and Technology Department (Grant No. 2021JQ-498), National Special Support Plan of China (Grant No. W02020331), and Chinese Academy of Forestry (Grant No. CAFYBB2021ZD002).

语种：英文

外文关键词：Wood identification; Headspace-gas chromatography-mass spectrometry (HS-GC-MS); Similarity network fusion (SNF); High-temperature heated wood; Direct analysis in real time-mass spectrometry (DART-MS)

摘要：Pterocarpus santalinus and Pterocarpus tinctorius are commonly used species of the genus Pterocarpus in the wood trade. Although both of them have been listed in Appendix II of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) since 2019, it is still critical to identify them in terms of plant taxonomy. Currently, high-temperature heating is an accepted treatment method for high-density wood species such as Pterocarpus to improve dimensional stability and restore previous drying defects partially. It has proved challenging to identify the high-temperature (e.g., 120 degrees C) heated wood from these two species. Thus, this study approaches species identification of two Pterocarpus of high-temperature (e.g., 120 degrees C) heated solid wood samples using headspace-gas chromatography-mass spectrometry (HS-GC-MS). Besides, a computational analytical method named similarity network fusion (SNF) was proposed to aggregate data in two different types, respectively, derived from the HS-GC-MS and direct analysis in real time-mass spectrometry (DART-MS) to explore the feasibility of improving the efficiency and accuracy of wood species discrimination. The SNF exhibits more significant differences and higher predictive accuracy (100%) between P. santalinus and P. tinctorius than that based on the HS-GC-MS data (77.78%) or DART-MS (66.67%) alone. These results demonstrated the capability of the HS-GC-MS technique in the analysis of high-temperature heated solid wood and the potential of multidimensional or comprehensive data sets based on the SNF algorithm in the field of wood species identification.