Screening of Miscanthus Genotypes for Sustainable Production of Microcrystalline Cellulose and Cellulose Nanocrystals

• Published in: Agronomy (Basel) Vol. 14; no. 6; p. 1255
• Main Authors: Liu, Weiming, You, Lanqing, Wang, Sheng, Li, Jie, Chen, Zhiyong, Si, Buchun, Iqbal, Yasir, Xue, Shuai, Fu, Tongcheng, Yi, Zili, Li, Meng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2024
• Subjects: Agricultural production; Algorithms; Biological materials; Biomass; biomass quality traits; Cellulose; cellulose nanocrystals; Chemical composition; Cloning; Comparative analysis; Correlation analysis; Crystalline cellulose; Crystals; Degree of polymerization; Genotypes; Germplasm; High-throughput screening; Industrial plants; Machine learning; microcrystalline cellulose; Miscanthus; Miscanthus spp; Nanocrystals; Particle size; Particle size distribution; Polymers; Scanning electron microscopy; Screening; Size distribution; Sustainable production; Zeta potential; Japan; China
• ISSN: 2073-4395; 2073-4395
• DOI: 10.3390/agronomy14061255

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.