文献类型：期刊文献

英文题名：Integrating terahertz time-domain spectroscopy with XGBoost for rapid and interpretable species-level wood identification of Pterocarpus

作者：Yu, Min[1] Chu, Jiawei[1] Wang, Jingjing[2] Yan, Jia[1] Xu, Peng[1] Qi, Hang[1] Liu, Shengquan[1] Zhou, Liang[1] Jiao, Lichao[3,4] Gao, Junlan[5]

第一作者：Yu, Min

通信作者：Yu, M[1];Gao, JL[2]

机构：[1]Anhui Agr Univ, Sch Mat & Chem, Key Lab Natl Forestry & Grassland Adm Wood Qual I, Hefei 230036, Peoples R China;[2]Comprehens Technol Ctr, Zhangjiagang Customs, Zhangjiagang, Peoples R China;[3]Chinese Acad Forestry, Res Inst Wood Ind, Beijing, Peoples R China;[4]Natl Forestry & Grassland Adm, Wood Specimen Resource Ctr, Beijing, Peoples R China;[5]Anhui Acad Agr Sci, Agr Mechanizat & Engn Res Inst, 230001 Hefei, Peoples R China

年份：2025

外文期刊名：WOOD MATERIAL SCIENCE & ENGINEERING

收录：;EI(收录号：20254719535666);Scopus(收录号：2-s2.0-105021948420);WOS:【SCI-EXPANDED(收录号:WOS:001614690300001)】；

基金：This work was supported by National Key Research and Development Program of China: [Grant Number 2023YFD2200501]; Young Talents Program of Anhui Academy of Agricultural Sciences: [Grant Number QNYC-202517].

语种：英文

外文关键词：Wood identification; terahertz time-domain spectroscopy; electromagnetic radiationt; feature selection; Shapley additive exPlanations

摘要：Overexploitation of global forest resources has led to severe survival pressure on endangered Pterocarpus species, and the illegal trade of their wood poses a serious threat to biodiversity and market order. To achieve accurate identification of Pterocarpus wood, this study integrated terahertz time-domain spectroscopy (THz-TDS) with gradient boosting algorithms to develop classification models for seven Pterocarpus wood species. The results showed that the XGBoost model performed best, achieving 100% accuracy in binary classification (P. santalinus and P. tinctorius) and 98.63% in three-class classification (P. indicus, P. macrocarpus, and P. soyauxii). After screening the THz frequency bands and performing feature selection on THz refractive indices using the Uninformative Variable Elimination (UVE) method, the seven-class classification accuracy of the constructed UVE-XGBoost model was improved to 88.64%, confirming that the 0.1-0.3 THz band is the most important frequency range for Pterocarpus wood classification models. SHAP interpretability analysis further revealed that 0.106 and 0.107 THz are the key characteristic frequencies for identifying the seven Pterocarpus wood species. This study demonstrates that THz-TDS combined with the XGBoost algorithm can achieve rapid and accurate identification of Pterocarpus wood, providing effective technical support for endangered wood protection and market supervision.