玉米秸秆组分近红外漫反射光谱(NIRS)测定方法的建立

Prediction of Maize Stover Components with Near Infrared Reflectance Spectroscopy

玉米秸秆是我国产量最大的秸秆生物质资源，但目前还没有快速高效的组分分析方法，本研究利用傅里叶变换近红外漫反射光谱（NIRS）技术，采用偏最小二乘法（PLS），在国内首次建立了NIRS测定玉米秸秆中灰分、半纤维素、纤维素、Klason木质素、酸不溶灰分和水分含量的校正模型，该模型稳定，适合不同地区、不同品种的玉米秸秆及其不同部位。实验结果表明，采用一阶导数＋KarlNorris滤波预处理，谱区在4100-7500cm^-1，能得到理想的预测模型。该模型对玉米秸秆各组分的交叉验证均方差（RMSECV）范围为0．0903～1．45，预测误差（RMSEP）范围为0．2569％～2．5819％，预测相关系数≥0．8711。该研究对加速我国秸秆生物质的工业转化具有重要意义。

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 4 100-7 500 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. 090 3 and 0. 547, respectively; the root mean square error of prediction （RMSEP） was 0. 774 6%, 1. 807 2%, 0. 256 9%, 2. 581 9%, 0. 515 8% and 1.032 5%, respectively. The models can be used to measure various samples in biomass transformation industry.