This study aims to realize the sharing of near-infrared analysis models of lignin and holocellulose content in pulp wood on two different batches of spectrometers and proposes a combined algorithm of SPA-DS,MCUVE-DS a...This study aims to realize the sharing of near-infrared analysis models of lignin and holocellulose content in pulp wood on two different batches of spectrometers and proposes a combined algorithm of SPA-DS,MCUVE-DS and SiPLS-DS.The Successive Projection Algorithm(SPA),the Monte-Carlo of Uninformative Variable Elimination(MCUVE)and the Synergy Interval Partial Least Squares(SiPLS)algorithms are respectively used to reduce the adverse effects of redundant information in the transmission process of the full spectrum DS algorithm model.These three algorithms can improve model transfer accuracy and efficiency and reduce the manpower and material consumption required for modeling.These results show that the modeling effects of the characteristic wavelengths screened by the SPA,MCUVE and SiPLS algorithms are all greatly improved compared with the full-spectrum modeling,in which the SPA-PLS result in the best prediction with RPDs above 6.5 for both components.The three wavelength selection methods combined with the DS algorithm are used to transfer the models of the two instruments.Among them,the MCUVE combined with the DS algorithm has the best transfer effect.After the model transfer,the RMSEP of lignin is 0.701,and the RMSEP of holocellulose is 0.839,which was improved significantly than the full-spectrum model transfer of 0.759 and 0.918.展开更多
对混胺燃料的近红外光谱分析模型的传递方法进行研究。采用K/S(Kennard/Stone)算法选择转换集样品,采用直接校正(Direct Standardization,DS)算法对从仪器采集的光谱进行校正。通过光谱平均差异(ARMS)比较奇异值分解(Singular Value Dec...对混胺燃料的近红外光谱分析模型的传递方法进行研究。采用K/S(Kennard/Stone)算法选择转换集样品,采用直接校正(Direct Standardization,DS)算法对从仪器采集的光谱进行校正。通过光谱平均差异(ARMS)比较奇异值分解(Singular Value Decomposition,SVD)算法和偏最小二乘法(Partial Least Squares,PLS)对光谱校正的效果。当PLS算法的最佳主因子数为3时,DS-PLS算法的光谱校正率可达到97.5%,优于DS-SVD算法。混胺样品的分析模型经过DS-PLS算法传递后,对从仪器的混胺样品各项指标的预测标准偏差(SEP)明显好于传递前,与主仪器预测效果接近,说明采用K/S算法选择合适的转换集样品后,通过DS-PLS模型传递算法可有效降低仪器间的光谱差异,实现近红外光谱分析模型在各台光谱仪之间共享。展开更多
基金The authors are grateful for the support of the Fundamental Research Funds of Research Institute of Forest New Technology,CAF(CAFYBB2019SY039).
文摘This study aims to realize the sharing of near-infrared analysis models of lignin and holocellulose content in pulp wood on two different batches of spectrometers and proposes a combined algorithm of SPA-DS,MCUVE-DS and SiPLS-DS.The Successive Projection Algorithm(SPA),the Monte-Carlo of Uninformative Variable Elimination(MCUVE)and the Synergy Interval Partial Least Squares(SiPLS)algorithms are respectively used to reduce the adverse effects of redundant information in the transmission process of the full spectrum DS algorithm model.These three algorithms can improve model transfer accuracy and efficiency and reduce the manpower and material consumption required for modeling.These results show that the modeling effects of the characteristic wavelengths screened by the SPA,MCUVE and SiPLS algorithms are all greatly improved compared with the full-spectrum modeling,in which the SPA-PLS result in the best prediction with RPDs above 6.5 for both components.The three wavelength selection methods combined with the DS algorithm are used to transfer the models of the two instruments.Among them,the MCUVE combined with the DS algorithm has the best transfer effect.After the model transfer,the RMSEP of lignin is 0.701,and the RMSEP of holocellulose is 0.839,which was improved significantly than the full-spectrum model transfer of 0.759 and 0.918.
文摘对混胺燃料的近红外光谱分析模型的传递方法进行研究。采用K/S(Kennard/Stone)算法选择转换集样品,采用直接校正(Direct Standardization,DS)算法对从仪器采集的光谱进行校正。通过光谱平均差异(ARMS)比较奇异值分解(Singular Value Decomposition,SVD)算法和偏最小二乘法(Partial Least Squares,PLS)对光谱校正的效果。当PLS算法的最佳主因子数为3时,DS-PLS算法的光谱校正率可达到97.5%,优于DS-SVD算法。混胺样品的分析模型经过DS-PLS算法传递后,对从仪器的混胺样品各项指标的预测标准偏差(SEP)明显好于传递前,与主仪器预测效果接近,说明采用K/S算法选择合适的转换集样品后,通过DS-PLS模型传递算法可有效降低仪器间的光谱差异,实现近红外光谱分析模型在各台光谱仪之间共享。
