| 摘要:用常规方法测定了4种常用制浆杨木的化学成分和基本密度,并采集了样品的近红外光谱。对光谱进行预处理后,运用偏最小二乘法和交互验证的方法,分别确定最佳主成分数并建立样品综纤维素、木素、苯-醇抽出物、基本密度的校正模型。独立验证中模型的决定系数（R2val）分别为0.9050、0.9098、0.9112、0.9165；预测均方根误差(RMSEP)分别为0.40%、0.42%、0.19%和0.0050 g/cm3；相对分析误差(RPD)分别为3.24、3.33、3.36和3.46；绝对偏差(AD)分别为-0.49%～0.77%、-0.66%～0.63%、-0.28%～0.33%、-0.0094～0.0068 g/cm3,预测均方根误差和绝对偏差基本符合对误差的要求,4个模型能够满足制浆造纸中常用杨木材性的快速测定。 |
| Abstract:The chemical composition and basic density of four species of poplar which were widely used as raw material in pulping were determined by using traditional methods and the near-infrared (NIR) spectra of the samples were also collected. Partial least squares (PLS) method and cross-validation were used to confirm the best principal component numbers and build the calibration models for holocellulose, lignin, benzene ethanol extractive and basic density of poplar samples. The independent verification of the calibration models showed the coefficients of determination (R2val) were 0.9050, 0.9098, 0.9112, 0.9165, respectively. The root mean square errors of prediction (RMSEP) were 0.40%, 0.42%, 0.19%, 0.0050 g/cm3, respectively. The relative percent deviations (RPD) were 3.24, 3.33, 3.36 and 3.46, respectively. And the absolute deviations (AD) were -0.49%～0.77%, -0.66%～0.63%, -0.28%～0.33%, -0.0094 g/cm3～0.0068 g/cm3, respectively. The root mean square error of prediction and the absolute deviation basically met the error requirement and the four calibration models could realize the rapid determination of the properties of poplar wood used in paper industry. |
