文献类型：期刊文献

英文题名：Development of a universal NIR model for predicting cellulose and lignin contents in multiple Pinus elliottii clones via efficient feature wavelength selection

作者：Liang, Long[1,2,3] Wu, Ting[1] Ma, Lingyu[4] Wang, Zhe[5] Deng, Yongjun[1] Zhu, Hongwei[3] Fang, Guigan[1]

第一作者：梁龙;Liang, Long

通信作者：Wu, T[1];Fang, GG[1]

机构：[1]Chinese Acad Forestry, Jiangsu Coinnovat Ctr Efficient Proc & Utilizat Fo, Int Innovat Ctr Forest Chem & Mat,Key Lab Biomass, Inst Chem Ind Forest Prod,Natl Key Lab Dev & Utili, Nanjing 210042, Jiangsu, Peoples R China;[2]South China Univ Technol, State Key Lab Adv Papermaking & Paper Based Mat, Guangzhou 510640, Peoples R China;[3]Yueyang Forest Paper Co Ltd, Yueyang 414002, Peoples R China;[4]Chinese Acad Forestry, Res Inst Wood Ind, Beijing 100091, Peoples R China;[5]Guangdong Acad Forestry, Guangdong Prov Key Lab Silviculture Protect & Util, Guangzhou 510520, Peoples R China

年份：2026

外文期刊名：HOLZFORSCHUNG

收录：;EI(收录号：20260419952811);Scopus(收录号：2-s2.0-105028011916);WOS:【SCI-EXPANDED(收录号:WOS:001663388600001)】；

基金：This work was funded by Biological Breeding-National Science and Technology Major Project (2023ZD0405905), State Key Laboratory of Advanced Papermaking and Paper-based Materials (South China University of Technology), no. 202311, and Jiangsu Province Forestry Science and Technology Innovation and Extension Project (LYKJ[2024]12).

语种：英文

外文关键词：Pinus elliottii; near infrared spectroscopy; chemical composition; feature wavelength selection; universal model

摘要：This study developed universal near-infrared (NIR) spectroscopy models for the high-throughput prediction of wood chemical composition across multiple Pinus elliottii clones. To overcome the limited generalizability of conventional full-spectrum models caused by clone-specific spectral variations and redundancy, three feature selection algorithms were evaluated for extracting robust, clone-independent spectral features correlated with cellulose and lignin content. The Monte Carlo successive projections algorithm (MC-SPA) proved most effective, constructing high-performance models using only a minimal subset of the full-spectrum wavelengths. The resulting universal models based on MC-SPA-selected features demonstrated improved predictive accuracy and generalizability across clones. This work highlights the importance of feature wavelength selection in building highly generalizable NIR calibrations and provides a practical strategy for high-throughput screening in multi-clone breeding programs.