基于近红外光谱法预测杨木的综纤维素含量

为快速测定人工林杨木的综纤维素含量,按国家标准测定了42 个杨木木材样品的综纤维素含量,并用近红 外光谱仪测定相应的光谱。在350 ~ 2 500、1 300 ~ 2 050、2 050 ~ 2 500 nm 3 个不同的光谱区域,采用未处理、 Baseline、一阶导数、二阶导数等光谱预处理方法,再用PLS1、PLS2、PCR 3 种不同建模方法建立相应的校正模型与 交互验证模型。结果表明:当光谱区域为1 300 ~2 050 nm、光谱数据未进行预处理、采用PLS1 的建模方法、主成分 数为8 时,建立的校正模型有最佳预测效果;采用建立的模型对未参与建模的样本进行预测,预测结果与实测结果 间的相关系数为0.818 8。 

Prediction of holocellulose content of poplar using near infrared spectroscopy

The holocellulose is one of the main chemical components of wood cell wall and has a critical effect on wood property andutilization. The holocellulose contents of 42 samples of poplar were determined by national standard of China, and then the near infrared (NIR) of all samples was collected by LabSpec Pro FR/ A114260 in this paper. The calibration and validation model were built using partial least squares (PLS1, PLS2) and principal component analysis (PCR) with different pretreatment methods of un- pretreatment, Baseline, the first derivative and the second derivative in different spectral regions of 350- 2 500 nm, 1 300-2 050 nm and 2 050-2 500 nm. The results showed that the best model was built by PLS1 with un-pretreated spectral data and 8 principal components in 1 300-2 050 nm. The coefficients of correlation (r), the root mean square error and the standard error of calibration model were 0.963 8, 0.006 3 and 0.006 4, respectively, and 0.647 1, 0.023 6 and 0.024 0 for validation model. The correlation (r) was 0.818 8 between the predicting and lab measuring values of the samples not involved in modeling.