相思树聚戊糖含量近红外光谱分析模型的建立及修正

建立适应不同产地相思树化学组分含量的近红外光谱分析模型对预测木材化学组分含量具有重要的意义。用常规化学法测量了取自广西78个和福建33个相思树样品的聚戊糖含量,并结合近红外光谱数据用偏最小二乘法建立了广西相思树聚戊糖含量的近红外光谱模型。校正模型的决定系数R2cv为0.947,内部交叉验证均方差RMSECV为0.464,验证模型Rv2al为0.925,RMSEP为0.455。为了扩大模型的适用范围,用福建不同数量样品对该模型进行修正。结果表明:在广西模型的基础上加入一个有代表性的福建样品就能大大降低直接用广西模型预测福建样品的误差。加入3个有代表性的福建样品后能够得到较好的模型。用该模型预测未参加建模的福建样品,预测模型的Rv2al为0.904,RMSEP为0.759。用4组(每组3个样)不同的福建样品修正广西模型,用固定的20个未参与修正广西模型的样品来验证,预测误差略有不同,表明样品的选择在一定程度上影响着修正模型的质量。

Extending Hemicelluloses Content Calibration of Acacia Spp Using NIR to New Sites

In this research,hemicellulose contents of 78 wood meal samples of Acacia spp trees grown in Guangxi and another 33 wood meal samples of Acacia spp trees grown in Fujian were measured by wet chemistry.NIR spectra were also collected by a Bruker MPA spectrometer within 4 000-12 500 cm-1 of wavenumbers using a standard sample cup.Equations were developed using partial least squares (PLS) regression and cross validation for multivariate calibration in this study.High coefficients of determination (R2) and low root mean square errors of cross-validation (RMSDCV) were obtained for hemicellulose content (R2=0.947,RIVISECV=0.464) of Guangxi wood meal samples.Prediction produced high correlation coeffieients between laboratory and predicted values,with R2 and RMSEP values being 0.925 and 0.455,respectively.A variable numbers of Fujian samples ranging from one to thirteen were used to enhance the Guangxi calibration so as to be widely used for routine assessment of wood chemistry.It was demonstrated that the addition of a single Fujian sample to the Guangxi calibration set was sufficient to greatly reduce predictive errors and that the inclusion of 3 Fujian samples in the Guangxi set was sufficient to give relatively stable predictive errors.The R2 is 0.904 and RMSEP is 0.759.The addition of different sets of 3 Fujian samples to the Guangxi calibration,however,caused predictive errors to vary between sets.