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| Application of Particle Swarm Optimization Based on Least Square Support Vector Machine in Quantitative Analysis of Klason Lignin of Mixed Wood Using Near-infrared Spectroscopy |
| Received:February 27, 2019 |
| DOI：10.11981/j.issn.1000-6842.2020.02.45 |
| Key Words:near-infrared spectroscopy;mixed pulping wood;Klason lignin;particle swarm optimization;least square support vector machine |
| 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 | E-mail | | XIONG Zhixin* | Jiangsu Provincial Key Lab of Pulp and Paper Science and Technology, Nanjing Forestry University, Nanjing, Jiangsu Province, 210037 | Leo_xzx@njfu.edu.cn | | LIU Yaoyao | Jiangsu Provincial Key Lab of Pulp and Paper Science and Technology, Nanjing Forestry University, Nanjing, Jiangsu Province, 210037 | | | WANG Yong | Jiangsu Provincial Key Lab of Pulp and Paper Science and Technology, 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: |
| To optimize the near-infrared analysis model of Klason lignin content of mixed wood, 82 samples from 5 common pulp woods were collected and prepared by grinding pretreatment, and their near-infrared spectrum signals were obtained via the portable spectrometer. The division of calibration set and testing set was followed by multiplication scatter correction (MSC). The parameters of least square support vector machines (LSSVM) were optimized by particle swarm optimization (PSO), and the selected parameters were used to establish LSSVM quantitative analysis model of Klason lignin content of mixed wood. The performance of PSO-LSSVM model was compared with partial least squares regression (PLS) model and principal component analysis combined with back propagation artificial neural network (PCA-BPNN) model. The calibration and validation results of PCA-BPNN and PSO-LSSVM were superior to PLS, and the PSO-LSSVM model showed the best performance. For PSO-LSSVM model, the correlation coefficients of validation (R v) was 0.9857 which was the largest among the three models. The root mean square error of prediction (RMSEP) of PSO-LSSVM model was 0.7498%, which was 0.2767% and 0.1455% lower than PLS model and PCA-BPNN model, respectively. The relative standard deviation (RPD) was 5.6174 which was improved 1.5144 comparing to the PLS model and 0.9138 comparing to PCA-BPNN model. And the maximum and minimum absolute errors (AD) were 0.0065%~1.8449%, which were the least among the three models. |
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