基于偏最小二乘法的CO_(2)加权组合测量研究

Research on CO_(2)weighted combination measurement based on partial least square method

为了解决光谱之间相互干扰、建模速度慢等缺点,采用多波段加权组合模型与偏最小二乘(partial least squares,PLS)法结合进行定量分析提高测量精度。本文搭建了基于超连续谱激光吸收光谱(spectrum laser absorption spectrum,SCLAS)技术的气体检测系统,基于PLS对近红外不同波段二氧化碳(CO_(2))进行加权组合测量研究。在常温常压下对1425—1443 nm、1565—1587 nm、1595—1616 nm波段内不同浓度CO_(2)的吸收光谱进行了测量,基于PLS建立了单一波段回归模型,得出的决定系数(coefficient of determination,R^(2))分别为0.9897、0.9486、0.9497。通过R^(2)和均方根误差(root mean square error,RMSE)分别确定单一波段模型的权重,采用多波段加权组合模型算法建立了新的PLS组合模型,得出的R^(2)分别为0.9852、0.9912。实验结果表明,基于PLS的加权组合模型能够提高CO_(2)浓度的预测精度与稳定性,有效避免建模速度慢和干扰问题。

In order to solve the shortcomings of mutual interference between spectra and slow modeling speed,a multi-band weighted combination model combined with the partial least squares(PLS)method was used for quantitative analysis to improve the measurement accuracy.In this paper,a gas detection system based on spectrum laser absorption spectrum(SCLAS)was built to perform a weighted combination measurement of CO_(2) in different wavelengths of the near-infrared based on PLS.The absorption spectra of different concentrations of CO_(2) in the bands of 1425—1443 nm,1565—1587 nm,and 1595—1616 nm were measured at room temperature and pressure.The single-band regression model based on PLS was established,and the coefficient of determination(R^(2))were 0.9897,0.9486 and 0.9497,respectively.The weights of the single band models are determined based on R^(2) and the root mean square error(RMSE).A new PLS combination model is established using the multi-band weighted combination model algorithm,and the obtained R^(2) are 0.9852 and 0.9912,respectively.The experimental results show that the PLS-based weighted combination model can improve the accuracy and stability of CO_(2) concentration prediction and effectively avoid the slow modeling speed and interference problems.