High-Throughput Analysis of Lignocellulosic Components in Miscanthus spp. Utilizing Near-Infrared Spectroscopy Integrated with Feature Selection Algorithms

Rapid, non-destructive assessment of biomass composition is essential for advancing Miscanthus spp. breeding and bioenergy production. This study aimed to develop and validate high-throughput near-infrared spectroscopy (NIRS) models for key chemical components in Miscanthus biomass. A robust calibration set was constructed from 107 diverse samples by combining two key species, Miscanthus sacchariflorus and M. lutarioriparius, to enhance chemical variability and create broadly applicable models. Partial Least Squares (PLS) regression models were developed using this dataset, comparing full-spectrum performance against models optimized with three feature selection algorithms: CARS, VCPA-GA, and VCPA-IRIV. All feature selection methods significantly enhanced predictive accuracy. Notably, the CARS-PLS models yielded excellent performance for cellulose (R2v = 0.98; RPD = 7.38), hemicellulose (R2v = 0.95, RPD = 4.35), lignin (R2v = 0.96, RPD = 5.40), and moisture (R2v = 0.98, RPD = 7.18), while the VCPA-IRIV-PLS model was superior for ash content (R2v = 0.96, RPD = 5.13). Overall, NIRS coupled with advanced feature selection provides a powerful, rapid protocol for Miscanthus biomass analysis, poised to accelerate germplasm evaluation and industrial quality control in the bioenergy sector.