Liu Li-ying, Chen Hong-zhang
State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100080, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2007 Feb;27(2):275-8.
The components concentrations in maize stover were analyzed with 67 samples selected from 380 samples of different provinces and varieties in order to serve the biomass utilization of our country. The technique of near infrared reflectance spectroscopy (NIRS) and partial least square (PLS) regression were used to establish the models. The results showed that the calibration models developed by the spectral data pretreatment of the first derivative+Karl Norris derivative filter were the best for ash, hemicellulose, cellulose, Klason lignin, acid unsolvable ash, and water in the spectral region of 4100-7500 cm(-1). The root mean square error of cross validation (RMSECV) for the above six components was 0.991, 1.27, 1.44, 0.599, 0.0903 and 0.547, respectively; the root mean square error of prediction (RMSEP) was 0.7746%, 1.8072%, 0.2569%, 2.5819%, 0.5158% and 1.0325%, respectively. The models can be used to measure various samples in biomass transformation industry.
为服务我国生物质利用，从380个来自不同省份和品种的样本中选取67个样本，分析了玉米秸秆中的成分浓度。采用近红外反射光谱（NIRS）技术和偏最小二乘（PLS）回归建立模型。结果表明，在4100 - 7500 cm(-1)光谱区域，经一阶导数+卡尔·诺里斯导数滤波的光谱数据预处理所建立的校正模型对灰分、半纤维素、纤维素、克拉森木质素、酸不溶性灰分和水分的效果最佳。上述六种成分的交叉验证均方根误差（RMSECV）分别为0.991、1.27、1.44、0.599、0.0903和0.547；预测均方根误差（RMSEP）分别为0.7746%、1.8072%、0.2569%、2.5819%、0.5158%和1.0325%。这些模型可用于测量生物质转化行业中的各种样本。
