Near infrared (NIR) spectroscopy can be implemented in the evaluation of cellulose. The potential of NIR spectroscopy combined with multivariate analysis to evaluate moisture variation in pulp was studied. Samples of four pulp types were conditioned to different moisture levels. The samples were air dried in a controlled environment, at each 10 % moisture reduction the material was weighed and analyzed with the NIR spectrometer. Principal Component Analysis (PCA) and Partial Least Squares Regression (PLS-R) were applied to the spectral signatures and moisture values obtained during drying. Combining NIR spectroscopy with PLS-R, the moisture of the pulps under different conditions was estimated with R²p ranging from 0.89 to 0.98 for independent validation and root mean square error (RMSEP) ranging from 5.1 % to 18.3 %. The PLS-R models were applied to NIR spectra taken from other pulps and the estimates were consistent. The models showed robustness for monitoring pulps subjected to moisture variations.

近红外 (NIR) 光谱可用于纤维素的评估。研究了近红外光谱结合多变量分析评估纸浆水分变化的潜力。四种纸浆类型的样品被调节到不同的水分含量。样品在受控环境中风干，每减少 10% 的水分，对材料进行称重并使用 NIR 光谱仪进行分析。主成分分析 (PCA) 和偏最小二乘回归 (PLS-R) 应用于干燥过程中获得的光谱特征和水分值。将 NIR 光谱与 PLS-R 相结合，估计不同条件下纸浆的水分，独立验证的 R²p 范围为 0.89 至 0.98，均方根误差 (RMSEP) 范围为 5.1 % 至 18.3 %。PLS-R 模型应用于从其他纸浆中获取的 NIR 光谱，估计值是一致的。这些模型显示了监测受水分变化影响的纸浆的稳健性。