In this study, polyploid Miscanthus lutarioriparius (PML) was introduced as a new sustainable and scalable source for cellulose nanocrystal (CNC). The agronomic traits of PML were significantly different from Miscanthus × giganteus (MG), but their chemical components and physical features were similar. Notably, a remarkable co-extraction of hemicellulose, lignin and ash and non-crystalline cellulose was observed during crude cellulose isolation from PML than it from MG by modified alkaline peroxide pretreatment. In addition, subjecting crude cellulose of PML and MG biomass to sulfuric acid hydrolysis provided high-quality CNC. The analysis of particle size distribution, zeta potential, crystalline index, the degree of polymerization, SEM and yield potential suggested that the CNC extracted from PML showed higher stability, processability and productivity than that from MG. Therefore, it provides a new theoretical basis for the applications of CNC prepared by PML and MG. The results also revealed potential genetic approaches for Miscanthus spp. to enhance biomass and CNC yield.

The pretreatment of lignocellulosic biomass such as Miscanthus grown on marginal agricultural land is very challenging and requires severe conditions to fractionate cell wall polymers for further valorization. The current study aimed to determine organic acid-based mild conditions to pretreat contrasting lignocellulosic Miscanthus genotypes for the efficient fractionation of cell wall components, with special focus on hemicellulose extraction. In doing so, five Miscanthus genotypes were subjected to four different acid treatments (sulfuric acid, oxalic acid, malonic acid, and citric acid) in a vertical high-pressure steam sterilizer. The results demonstrated that, among the organic acids, oxalic acid was identified as the most effective pretreatment solvent for hemicellulose separation, whereas citric acid yielded the highest amount of galacturonic acid, varying from 15 to 17 mg mL⁻¹ across genotypes. One best performing genotype was selected for the enzymatic hydrolysis. Overall, M. floridulus genotypes exhibited the optimal quality traits for efficient bioconversion with second best in terms of ethanol production potential.

Miscanthus spp. has been regarded as a promising industrial plant for the sustainable production of bio-based materials. To assess its potential for microcrystalline cellulose (MCC) and cellulose nanocrystals (CNCs) production, 50 representative clones of M. sinensis and M. floridulus were selected from a nationwide collection showcasing the extensive diversity of germplasm resources. Descriptive analysis indicates that the dry biomass weight of M. floridulus is advantageous whereas M. sinensis demonstrates higher MCC and CNCs yields as well as a smaller CNCs particle size. Correlation analyses indicated that MCC yield is solely influenced by the cellulose content whereas the yield of CNCs is affected by both the cellulose content and CrI. Comparative analyses of the chemical composition, physical features (degree of polymerization, crystalline index, particle size distribution and zeta potential), and scanning electron microscopy indicated that the MCC and CNCs extracted from M. sinensis and M. floridulus exhibited remarkable stability and quality. Additionally, the CNCs derived from M. sinensis and M. floridulus exhibited a distinctive ball-shaped structure. Notably, machine learning has demonstrated its efficacy and effectiveness in the high-throughput screening of large populations of Miscanthus spp. for predicting the yield of MCC and CNCs. Our results have also laid the theoretical foundation for the exploration, cultivation, and genetic breeding of M. sinensis and M. floridulus germplasm resources with the purpose of MCC and CNCs preparation.

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.