近红外光谱结合连续投影算法检测综纤维素含量

熊智新; 马璞璠; 梁　龙; 房桂干

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Determination of Holocellulose Content Using Near Infrared Spectroscopy and Successive Projections Algorithm

DOI：10.11981/j.issn.1000-6842.2019.04.46

Key Words:near infrared spectroscopy; SPA; pulping wood materials; holocellulose content

Fund Project:国家林业局“948”项目“农林剩余物制机械浆节能和减量技术引进”（2014 4-31）。

Author Name	Affiliation
XIONG Zhixin	Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing, Jiangsu Province, 210037 College of Light Industry and Food Engineering, Nanjing Forestry University, Nanjing, Jiangsu Province, 210037
MA Pufan	Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing, Jiangsu Province, 210037 College of Light Industry and Food Engineering, Nanjing Forestry University, Nanjing, Jiangsu Province, 210037
LIANG Long	Institution of Chemical Industry of Forestry Products, CAF, Nanjing, Jiangsu Province, 210042
FANG Guigan	Institution of Chemical Industry of Forestry Products, CAF, Nanjing, Jiangsu Province, 210042

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Abstract:

In order to simplify the model and realize the rapid and accurate determination of the pulping wood’s holocellulose content by near infrared spectroscopy， the effective wavelength combination was selected by successive projections algorithm （SPA） to conduct research and analysis of modeling experiment. A total of 82 samples of 5 kinds of pulpwood were prepared to measure the holocellulose content and sample spectral data. After removing the outliers by Monte Carlo cross validation method， all remaining samples were split into calibration and prediction sets by the ratio of 2∶1. The calibration sets was pretreated by MSC method， and using SPA to select wavelengths， a near infrared analysis model of the holocellulose content was established by partial least square （PLS） regression. The model performance was compared with that of the correlation coefficient method as well as competitive adaptive reweighed sampling （CARS） algorithm. The results showed that the 25 wavelengths selected by the SPA could fully characterize the holocellulose content information hidden in the whole spectrum. The PLS regression model based on SPA had the highest prediction accuracy with RMSEP=0.8306 and R2p=0.9801 respectively，which met the precision requirements of industrial applications. The investigation also provided a more effective method for rapid determination of the holocellulose content of pulp woods．

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