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| Prediction of Basic Density of Wood Chips Using Near-Infrared
Spectroscopy and Moisture Content Correction Algorithm |
| LIANG Long1, 2, 3, 4, WU Ting1, 4*, SHEN Kui-zhong1, XIONG Zhi-xin5, XU Feng2*, FANG Gui-gan1* |
1. Institute of Chemical Industry of Forest Products, Chinese Academy of Forestry; Key Lab of Biomass Energy and Material, Jiangsu Province; Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Jiangsu Province; Key Lab of Chemical Engineering of Forest Products, National Forestry and Grassland Administration; National Engineering Research Center of Low-Carbon Processing and Utilization of Forest Biomass, Nanjing 210042, China
2. College of Materials Science and Technology, Beijing Forestry University，Beijing Key Laboratory of Lignocellulosic Chemistry, Beijing 100083, China
3. Institute of Ecological Conservation and Restoration, Chinese Academy of Forestry, Beijing 100091, China
4. Key Laboratory of Chemistry and Engineering of Forest Products, State Ethnic Affairs Commission，Guangxi Key Laboratory of Chemistry and Engineering of Forest Products, Guangxi University for Nationalities, Nanning 530006, China
5. College of Light Industry and Food Engineering, Nanjing Forestry University, Nanjing 210037, China
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Abstract Wood basic density is an important indicator for assessing the pulping properties of raw wood materials. Rapidly determining the basic density of wood chips using near-infrared spectroscopy (NIRS) can provide basic theoretical data for developing and optimising pulp production processes. However, the source complicacy of raw material leads to high variability within the moisture content of wood chips. These fluctuations in the raw material make it difficult for the NIRS model to give a stable prediction performance. In this paper, the moisture desorption process of poplar chips was dynamically monitored by near-infrared spectroscopy. Principal component analysis (PCA) was applied to distinguish the spectral features due to moisture content to explore the change of free water and bound water in wood fiber. In order to investigate the effect of moisture content on the NIRS prediction of wood density, the partial least square calibration (PLS) models were built using wood chips with different moisture content conditions, respectively. And then external parameter orthogonalization algorithm (EPO) was used to improve the robustness of predictive models by eliminating the influence of chip moisture. The results showed that the best prediction accuracy was obtained from water-saturated chips spectra due to full access to information about fiber structures. However, much water absorption information in the spectra was redundant and useless for modeling, and the variations in moisture content also led to unstable prediction performance. The spectral moisture correction based on EPO was an effective method for desensitizing the calibration model to the influence of moisture content, enabling robust and accurate prediction of basic density. The EPO-PLS model provided a performance with a root mean square error (RMSE) of 12.23 kg·m-3, determination coefficients (R2) of 0.883 4, and residual prediction deviation (RPD) of 2.93 under different moisture content. This study built a robust NIR calibration model which was robustified against the influence of the variations in moisture content on the wood density prediction. This technology may facilitate the expansion of potential applications of NIR spectroscopy in the paper and pulp industry.
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Received: 2022-04-26
Accepted: 2022-08-16
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Corresponding Authors:
WU Ting, XU Feng, FANG Gui-gan
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