Efficiency of Near-Infrared Spectroscopy in Quantifying Lignin in Black Liquor-Impregnated Reforestation Wood

Cellulose and lignin are biopolymers with significant potential for chemical synthesis and energy production; however, their heterogeneous composition presents challenges for their use as raw material sources. This study employed near-infrared (NIR) spectroscopy coupled with partial least-squares regression (PLSR) to predict cellulose and lignin content in sapwood and heartwood of <i>Eucalyptus urophylla</i> and sapwood of <i>Pinus taeda</i>, all impregnated with black liquor under high pressure. Samples were analyzed across three longitudinal sections (top, middle, base), with no significant compositional variation detected. Near-infrared spectral data (1100-2500 nm) and pre-processed using the standard normal variate (SNV) method, yielded high predictive accuracy: <i>R</i>² values of 0.98-0.99 for cellulose and 0.94-0.96 for lignin, with root mean square error (<i>RMSE</i>) values of 0.2-0.3 and 0.1, respectively. Principal component analysis (PCA) explained 98% of sample variance, revealing clear distinctions between <i>E. urophylla</i> sapwood and heartwood. These findings confirm the efficacy of NIR-PLSR as a nondestructive, reliable alternative to conventional chemical analyses, with implications for improved quality control and decision-making in the wood treatment industry.