Compositional characterization of biomass is vital for the biofuel industry. Traditional wet chemistry-based methods for analyzing biomass composition are laborious, time-consuming, and require extensive use of chemical reagents as well as highly skilled personnel. In this study, near-infrared (NIR) spectroscopy was used to quickly assess the composition of above-ground vegetative biomass from 113 diverse, photoperiod-sensitive, biomass-type sorghum ) accessions cultivated under field conditions in Central Illinois. Biomass samples were analyzed using NIR spectra collected in the spectral range of 867–2536 nm, with their chemical compositions determined following the National Renewable Energy Laboratory (NREL) protocol. Advanced spectral pre-treatment and band selection techniques were utilized to develop calibration models using partial least squares regression (PLSR). The models' effectiveness was assessed through cross-validation and independent data tests. The predictions for moisture, ash, extractives, glucan, xylan, acid-soluble lignin (ASL), acid-insoluble lignin (AIL), and total lignin were accurate and reliable, demonstrating the capability of NIR spectroscopy to provide rapid and precise characterization of sorghum biomass. The results demonstrated that NIR spectroscopy is an efficient tool for rapidly characterizing sorghum biomass, making it a sustainable option for screening desirable feedstock for biofuel or bioproduct production.

中文翻译：

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使用近红外光谱和化学计量学快速、高通量测定高粱（高粱）生物量成分


生物质的成分表征对于生物燃料行业至关重要。传统的基于湿化学的生物质成分分析方法费力、耗时，并且需要大量使用化学试剂以及高技能人员。在这项研究中，使用近红外 (NIR) 光谱法快速评估伊利诺伊州中部田间条件下种植的 113 种不同的、光周期敏感的生物量型高粱种质的地上营养生物量的组成。使用在 867–2536 nm 光谱范围内收集的近红外光谱对生物质样品进行分析，并根据国家可再生能源实验室 (NREL) 协议确定其化学成分。利用先进的光谱预处理和波段选择技术，使用偏最小二乘回归 (PLSR) 开发校准模型。通过交叉验证和独立数据测试评估模型的有效性。对水分、灰分、提取物、葡聚糖、木聚糖、酸溶性木质素 (ASL)、酸不溶性木质素 (AIL) 和总木质素的预测准确可靠，证明了近红外光谱能够快速、精确地表征高粱生物量。结果表明，近红外光谱是快速表征高粱生物质的有效工具，使其成为筛选生物燃料或生物产品生产所需原料的可持续选择。