Rapid chemical characterization of loblolly pine forest residues with near-infrared hyperspectral imaging
| Authors: | Sameen Raut, Ighoyivwi Onakpoma, Seyedehsan Vasefi, Joseph Dahlen, Laurence R. Schimleck, Sudhagar Mani, Gerald Presley, Thomas L. Eberhardt |
| Year: | 2026 |
| Type: | Scientific Journal |
| Station: | Forest Products Laboratory |
| DOI: | https://doi.org/10.1016/j.biombioe.2025.108881 |
| Source: | Biomass and Bioenergy |
Abstract
Loblolly pine (Pinus taeda) is the most important commercial tree species in southeastern U.S. In recent years, there has been a growing interest in utilizing loblolly pine wood and harvest residues consisting of bark, branches, needles, and wood, as a viable renewable feedstock to meet growing energy, chemical, and biomaterial demand. Feedstock chemical compositions affect the conversion efficiency and product yields, but these properties are costly and laborious to measure. Calibration models capable of rapid and precise estimation of these properties were developed using near-infrared (NIR) hyperspectral imaging data from southern pine forest harvest residues collected from different physiographic regions and age groups. Partial least squares regression models showed good cross-validation fit statistics for most measured properties, with 6 of 12 having Rc2v > 0.9, thus demonstrating that NIR hyperspectral imaging is a rapid and reliable tool for estimation of chemical composition. The results reported here have important implications in rapid feedstock suitability analysis for conversion of southern pine harvest residues to biofuels and bioproducts.