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

作者：Long Liang, Ting Wu, Lingyu Ma, Zhe Wang, Yongjun Deng, Hongwei Zhu, Guigan Fang · 发表于：Holzforschung · 年份：2026 · DOI：10.1515/hf-2025-0109 · 被引用次数：2 · 研究领域：Spectroscopy and Chemometric Analyses、Wood Treatment and Properties、Remote Sensing and LiDAR Applications

Abstract 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.