Influence of particle size on NIR spectroscopic characterization of sorghum biomass for the biofuel industry

Journal Article · 01 January 2025 · Results in Chemistry

DOI: https://doi.org/10.1016/j.rechem.2024.102016 · OSTI ID:2502094

Ahmed, Md Wadud ^[1]; Esquerre, Carlos A. ^[1]; Eilts, Kristen ^[1]; Allen, Dylan P. ^[1]; McCoy, Scott M. ^[1]; Varela, Sebastian ^[2]; Singh, Vijay ^[1]; Leakey, Andrew D. B. ^[1]; Kamruzzaman, Mohammed  ^[1]

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1. University of Illinois Urbana-Champaign, IL (United States)
2. University of Illinois Urbana-Champaign, IL (United States); Varela Quintela, Canelones (Uruguay)

NIR spectroscopy is a rapid and accurate green technology for high-throughput biomass characterization, including sorghum (Sorghum bicolor), a promising energy crop for the biofuel industry. This study assessed the influence of particle size on NIR spectroscopic analysis (wavelength range: 867–2535 nm) of sorghum biomass composition. Grown under field conditions, a total of 113 types of genetically diverse sorghum accessions were dried, ground, and sieved (<250, 250–600, 600–850, and > 850 µm particle size) for developing partial least square regression (PLSR) prediction models for moisture, ash, extractive, glucan, xylan, acid-soluble lignin (ASL), acid-insoluble lignin (AIL), and total lignin (ASL + AIL). Overall, smaller particle sizes provided better model performance, while no single particle size provided the best performance for all the selected components. With only 9 selected bands and 4 latent variables (LVs), the best PLSR model was obtained for moisture with particle size of 600–850 µm with the square root of the coefficient of determination (R) of 0.85, the ratio of prediction to deviation (RPD) of 2.2, and the root mean square error (RMSE) of 0.46 % in external validation. Similar model performances were also obtained for ash, extractive, glucan, and xylan. This study showed that size reduction could effectively improve NIR spectroscopic analysis for lipid-producing sorghum biomass for the biofuel industry.

• Research Organization: Center for Advanced Bioenergy and Bioproducts Innovation (CABBI), Urbana, IL (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Biological and Environmental Research (BER)
• Grant/Contract Number: SC0018420
• OSTI ID: 2502094
• Journal Information: Results in Chemistry, Journal Name: Results in Chemistry Vol. 13; ISSN 2211-7156
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English