Abstract：
In this study, in order to realize the sharing of the near-infrared analysis model of holocellulose between three spectral instruments of the same type, 84 pulp samples and their content of holocellulose were taken as the research objects. The effects of 10 pre-processing methods, such as 1st derivative (D1st), 2nd derivative (D2nd), multiplicative scatter correction (MSC), standard normal variable transformation (SNV), autoscaling, normalization, mean centering and pairwise combination, on the transfer effect of the stable wavelength selected by screening wavelengths with consistent and stable signals (SWCSS) were discussed. The results showed that the model established by the wavelength selected by the SWCSS algorithm after the autoscaling pre-processing method had the best analysis effect on the two target samples. Root mean square error of prediction (RMSEP) decreased from 2.4769 and 2.3119 before the model transfer to 1.2563 and 1.2384, respectively. Compared with the full-spectrum model, the value of AIC decreased from 3209.83 to 942.82. Therefore, the autoscaling pre-processing method combined with SWCSS algorithm can significantly improve the accuracy and efficiency of model transfer and provide help for the application of SWCSS algorithm in the rapid determination of pulp properties by near-infrared spectroscopy (NIRS).
摘要：
在这项研究中,为了实现同类型三种光谱仪器之间全纤维素近红外分析模型的共享,以84个纸浆样品及其全纤维素含量为研究对象。10种预处理方法的影响，如一阶导数(D1)，二阶导数（D2），乘法散射校正(MSC)，标准正态变量变换(SNV)，自动缩放，归一化，平均居中和成对组合，讨论了通过筛选具有一致和稳定信号的波长（SWCSS）选择的稳定波长的传输效果。结果表明,采用自动定标预处理方法后的SWCSS算法选取的波长所建立的模型对两个目标样本的分析效果最好。预测均方根误差（RMSEP）从模型转换前的2.4769和2.3119分别降低到1.2563和1.2384。与全谱模型相比，AIC值从3209.83降至942.82。因此,该自动定标预处理方法结合SWCSS算法可以显著提高模型传递的准确性和效率,为SWCSS算法在近红外光谱(NIRS)快速测定纸浆性质中的应用提供帮助。
