Abstract Application of near infrared (NIR) spectroscopy to pulp and paper research is almost based on chemical pulping, but little research has been directed toward the use of NIR to mechanical pulping. Compared with chemical pulp, mechanical pulp exhibits certain attractive qualities such as high pulping yield, sufficient opacity and bulk. However, because non-cellulose components (hemicelluloses and lignin) are preserved largely in the produced fibre, mechanical pulping process emerges easily production problem, such as inefficient chemical impregnation and unstable pulp properties. Therefore, it is necessary to explore a rapid measurement technique suitable for pulping process control. This study investigated the feasibility of NIR to predict rapidly the strength properties of mechanical pulp. Alkaline peroxide mechanical pulp (APMP), using poplar as raw material, was produced at pilot plant scale, different type of mechanical compressive pre-treatment and different pulping degree were implemented to increase variability in the obtained pulp samples. NIR spectra were collected from the rough and smooth surface of pulp handsheets to develop calibration models for tensile, burst, and tear index. Calibration statistics results indicated that rough surface spectra could provide more accurate and robust calibrations compared with smooth surface spectra. Additionally, spectral signal correction was proved to be a critical step for NIR calibration optimization. Based on spectra collected with multiplicative scatter correction and derivatives pre-processing, excellent NIR prediction performance was obtained with the root mean square error of 1.776 Nm g−1 for tensile index, 0.108 kPam2 g−1 for burst index and 0.116 mNm2 g−1 for tear index, the RPD values greater than 2.0 satisfying quantitative analysis.

中文翻译：

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利用近红外光谱和多元校准预测杨树碱性过氧化物机械浆的强度特性

摘要 近红外 (NIR) 光谱在纸浆和造纸研究中的应用几乎基于化学制浆，但很少有研究针对将 NIR 用于机械制浆。与化学纸浆相比，机械纸浆具有一定的吸引人的品质，例如制浆率高、不透明性和松密度足够。然而，由于非纤维素成分（半纤维素和木质素）大部分保留在生产的纤维中，机械制浆过程容易出现生产问题，如化学浸渍效率低和纸浆性能不稳定。因此，有必要探索一种适用于制浆过程控制的快速测量技术。本研究调查了 NIR 快速预测机械浆强度特性的可行性。碱性过氧化物机械浆（APMP），以杨树为原料，以中试规模生产，实施不同类型的机械压缩预处理和不同的制浆程度，以增加获得的纸浆样品的可变性。从纸浆手抄纸的粗糙和光滑表面收集 NIR 光谱，以开发拉伸、破裂和撕裂指数的校准模型。校准统计结果表明，与光滑表面光谱相比，粗糙表面光谱可以提供更准确和稳健的校准。此外，光谱信号校正被证明是 NIR 校准优化的关键步骤。基于通过乘法散射校正和导数预处理收集的光谱，获得了出色的 NIR 预测性能，拉伸指数为 0 时的均方根误差为 1.776 Nm g-1。