我国生物质能源产业近年来得到快速发展, 但对能源草的研究还处于初级阶段, 如果能建立全面的能源植物木质素、 纤维素、 半纤维素的近红外预测模型数据库, 将有助于优良品种的筛选、 能源植物能用性能的评价及生物质能源产业在线控制。 本研究采用傅里叶变换近红外光谱(FT-NIR)技术结合偏最小二乘法(PLSR)建立了荻、 南荻、 奇岗、 芒四种芒属能源植物品质指标(纤维素, 半纤维素, 木质素和灰分)近红外预测模型, 并在此基础上研究了样本粒度对模型的影响。 研究结果表明: (1)四种芒属能源植物茎秆中纤维素, 半纤维素和木质素含量误差均方根(RMSECV)分别为1.35％(R2=0.88), 0.39%(R2=0.91)和0.35%(R2=0.80), 叶片中纤维素, 半纤维素和木质素含量误差均方根(RMSECV)分别为0.72%(R2=0.88), 0.85%(R2=0.85)和0.44 (R2=0.87), 所建的纤维素, 半纤维素和木质素的近红外校准模型在预测未知样品含量时效果较好, 但灰分含量预测效果不理想; (2)2和0.5 mm粒度样品所建近红外模型均满足样品检测精度要求, 但考虑到时间和人工成本, 建议在工厂对能源植物原料品质进行分析时, 采用2 mm样品建模。

Biomass energy is being industrialized rapidly in China in recent years, whereas, research on energy grass is still in primary stage. Only if near-infrared spectroscopy mode was constructed which was used to predict the lignin, cellulose and hemicellulose contents in energy crop, the varieties screening, performance evaluation and on-line control of industrialization would be facilitated. In this study, the prediction model for quality indices (cellulose, hemicellulose, lignin and ash) of four energy grass (Miscanthus) was built using Fourier transform near-infrared (FT-NIR) spectroscopy combined with partial least squares regression (PLSR) , and the impacts exerted by particle size on the model were also revealed. The results showed that (1) the root mean error of cross validation (RMSECV) of cellulose, hemicelluloses and lignin contents were 1.35% (R2=0.88), 0.39% (R2=0.91) and 0.35 (R2=0.80), respectively in stalk and 0.72% (R2=0.88), 0.85% (R2=0.85) and 0.44 (R2=0.87), respectively in leaf. The model showed good performance in prediction of corresponding contents in unknown samples, however, no satisfying performance in ash content. (2) Both 2 mm and 0.5 mm grades of particle size can meet accuracy requirements of the model. But considering the time and labor cost, 2 mm grade was suggested for model building.