提出一种采用近红外光谱测定天然纤维素(棉、 木)浆粕聚合度的方法。 实验收集了195个天然纤维素浆粕样品, 采用GB/T 9107-1999方法测定了其聚合度, 对样品进行粉碎预处理后, 放入旋转杯中采集相应的近红外漫反射光谱。 通过化学计量学偏最小二乘方法(PLS)将聚合度数据与近红外光谱关联, 分别建立了棉木浆粕混合样品、 棉浆粕样品和木浆粕样品的聚合度定量模型。 最优模型相关系数分别为0.980, 0.993, 和0.886, 预测均方根误差(RMSEP)分别为147, 143和53。 研究了近红外方法精密度和准确性。 结果表明棉浆粕和木浆粕分类模型预测准确性优于混合模型, 且其预测精密度符合国标(GB)方法要求。 另外, 采用主成分分析方法建立了棉浆粕和木浆粕的识别模型, 结果表明该模型可以有效识别棉浆粕和木浆粕。 该方法操作简单、 分析速度快, 能够满足天然纤维素浆粕聚合度在线检测要求, 为实现清洁制浆新工艺聚合度在线监控目标提供了理论和技术依据。

A new method of near-infrared (NIR) diffuse reflectance spectroscopy is proposed to rapidly determine the degree of polymerization (DP) of natural cellulose (cotton and wood) pulp produced by a new clean pulping process. One hundred and ninety five samples were collected and their DP data were determined by standard method GB/T 9107—1999. The spectroscopy measurement method of the samples was studied and their near-infrared diffuse reflectance spectra were collected. The quantitative DP calibration models of one mixed cotton & wood and two separate cotton and wood pulps were established by partial least squares (PLS). The optimum models were developed using the spectra pretreated by derivative, autoscaling and mean-centering, and their performance is as follows: correlation coefficient of 0.980, 0.993 and 0.886, and RMSEP of 147, 143 and 53, respectively. The accuracy of NIR method was also studied. The results show that the accuracy of the two separate models of cotton and wood is better than that of the mixed model, and the precision of the two separate models is better than that of GB/T9107—1999. The identification model of cotton and wood was also established using principal component analysis (PCA). The result shows that the spectra of cotton and wood pulp have obvious difference, and the model can identify successfully the two kinds of pulp. The result indicates that the new NIR method is feasible to realize the on-line analysis of polymerization degree of natural cellulose pulp with its advantage of rapidness and easy operation.