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一种基于吸光度数学模型的多组分气体波长选择方法。

A multicomponent gas wavelength selection method based on an absorbance mathematical model.

作者信息

Li Honglian, Yan Xiangyu, Yao Yuhang, Liang Yujiao, Li Wenduo, Fang Lide

机构信息

School of Quality and Technical Supervision, Hebei University, Baoding, 071002, China.

出版信息

Anal Methods. 2023 Feb 2;15(5):572-580. doi: 10.1039/d2ay01760a.

Abstract

In order to quickly and accurately determine the optimal absorption spectra of the gases to be measured, a method for selecting the optimal wavelengths for multicomponent gases was proposed. A mathematical model of the absorbance of multicomponent gases was established, and the selection conditions of the optimal wavelengths were analyzed from the perspective of geometric significance. The best measurement spectra for the gas mixture of CO, CH and CH were determined and the gas mixture was measured using the supercontinuum laser absorption spectroscopy (SCLAS) technique, and the partial least squares (PLS) model and the least squares (LS) model were established to quantify the experimental results. The results showed that the PLS model had better prediction performance. The root mean square error (RMSE) of the calibration set PLS model for CO and CH was 0.1652 and 0.0053, the RMSE of the prediction set PLS model was 0.1991 and 0.0163, and the determination coefficient () of the models was above 0.9. The experimental results show that the optimal wavelength selection method for multicomponent gases proposed in this study can effectively determine the optimal measurement spectral lines for the gases to be measured.

摘要

为了快速准确地确定被测气体的最佳吸收光谱,提出了一种多组分气体最佳波长选择方法。建立了多组分气体吸光度的数学模型,并从几何意义的角度分析了最佳波长的选择条件。确定了CO、CH和CH混合气体的最佳测量光谱,采用超连续谱激光吸收光谱(SCLAS)技术对混合气体进行测量,并建立了偏最小二乘(PLS)模型和最小二乘(LS)模型对实验结果进行量化。结果表明,PLS模型具有较好的预测性能。CO和CH校准集PLS模型的均方根误差(RMSE)分别为0.1652和0.0053,预测集PLS模型的RMSE分别为0.1991和0.0163,模型的决定系数()均在0.9以上。实验结果表明,本研究提出的多组分气体最佳波长选择方法能够有效地确定被测气体的最佳测量谱线。

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