High-throughput evaluation of lignocellulosic biomass feedstock quality is the key to the successful commercialization of bioethanol production. Currently, wet chemical methods for the determination of chemical composition and biomass digestibility are expensive and time-consuming, thus hindering comprehensive feedstock quality assessments based on these biomass specifications. To find the ideal bioethanol feedstock, we perform a near-infrared spectroscopic (NIRS) assay to rapidly and comprehensively analyze the chemical composition and biomass digestibility of 59 Jerusalem artichoke (Helianthus tuberosus L., abbreviated JA) clones collected from 24 provinces in six regions of China. The distinct geographical distribution of JA accessions generated varied chemical composition as well as related biomass digestibility (after soluble sugars extraction and mild alkali pretreatment). Notably, the soluble sugars, cellulose, hemicellulose, lignin, ash, and released hexoses, pentoses, and total carbohydrates were rapidly and perfectly predicted by partial least squares regression coupled with model population analyses (MPA), which exhibited significantly higher predictive performance than controls. Subsequently, grey relational grade analysis was employed to correlate chemical composition and biomass digestibility with feedstock quality score (FQS), resulting in the assignment of tested JA clones to five feedstock quality grades (FQGs). Ultimately, the FQGs of JA clones were successfully classified using partial least squares-discriminant analysis model coupled with MPA, attaining a significantly higher correct rate of 97.8% in the calibration subset and 91.1% in the validation subset. Based on the diversity of JA clones, the present study has not only rapidly and precisely examined the biomass composition and digestibility with MPA-optimized NIRS models but has also selected the ideal JA clones according to FQS. This method provides a new insight into the selection of ideal bioethanol feedstock for high-efficiency bioethanol production.

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 lutarioriparius has a growing area of 100,000 ha and an annual biomass production of 1 Mt around Dongting Lake. However, due to serious soil pollution, there is a concern that the M. lutarioriparius biomass could have high heavy metal (HM) concentrations. This necessitates investigation of biomass quality to find the appropriate end use. Thus, this study aims to investigate the dynamics of HM elements in the M. lutarioriparius biomass and their impact on biomass quality across different growing areas and harvest times. We analyzed the HM concentrations in soil and biomass from 11 sites under different harvesting times (April, August and December). Results showed that Cd in soil samples was 9.43-fold higher than the national standards. The heavily polluted soil caused a high HM concentration in the biomass and the accumulation increased with the delayed harvest. The fresh young shoots in April met the food limitation for Cd and Cr, whereas Pb concentration was slightly higher than the threshold limit. The mature biomass from the southern part had higher Mn, Cd and Pb, but lower Cu, Zn and Cr concentrations than that from the eastern part. These results can provide guidance for guaranteeing the consistent quality of the M. lutarioriparius biomass for bio-based industry.

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.

Here, we report the complete genome sequence of an H6N6 avian influenza virus (AIV) isolated from a pigeon in Guangxi, southern China, in 2014. The eight RNA segment genes shared a high nucleotide identity (97 to 99%) with H6N6 subtypes of AIV isolated from ducks in the regions around Guangxi Province. The finding of this study will help us understand the ecology and molecular characteristics of H6 avian influenza virus in wild birds in southern China.