利用Tikhonov正则化算法进行光谱特征波长的选择及其参数优化

在烷烃类多组分混合气体，尤其轻烷烃类气体傅里叶变换红外光谱定量分析中，其中在红外光谱区域吸收峰严重交叉重叠，不易建立定量分析模型。为此，采用Tikhonov正则化算法对甲烷、乙烷、丙烷、异丁烷、正丁烷、异戊烷和正戊烷等七种轻烷烃类混合气体傅里叶变换红外光谱进行特征波长的选择，以便建立定量分析模型。选择六种各气体浓度组成混合烷烃气体，采用Tikhonov正则化算法，通过对比分析混合气体在中红外全波段、主吸收峰和次吸收峰波段特征波长的选择和TR参数的优化，选择出七种气体成分的傅里叶变换红外光谱的特征波长。利用选择的特征波长和Tikhonov正则化参数对实测甲烷光谱数据进行检验分析，与其他气体成分的交叉灵敏度最大为11.153 7%，最小为1.239 7%，预测均方根误差为0.004 8，有效增强了Tikhonov正则化算法在轻烷烃类混合气体定量分析中的实用性，初步验证了利用Tikhonov正则化进行烷烃类混合气体傅里叶变换红外光谱特征波长选择的可行性。

The Spectral Characteristic Wavelength Selection and Parameter Optimization Based on Tikhonov Regularization

In the multicomponent mixture hydrocarbon gases Fourier transform infrared (FTIR) quantitative analysis, especially for light alkane gases, it is not easy to establish the quantitative analysis model because their IR spectra absorption peaks are seriously overlapped. Aiming at this problem, the Tikhonov regularization algorithm was used to select the characteristic wavelengths for seven kinds of light alkane mixture gases FTIR which are composed with methane, ethane, propane, iso-butane, n-butane, iso-pentane and n-pentane. And then the wavelength selection was used to establish the quantitative analysis model. By comparing the analysis characteristics wavelength selection and TR parameters optimization of the mixed gases in the infrared all wave band, the first absorption peak band and the second peak band, the characteristic wavelength of 7 kinds of gases were selected by Tikhonov algorithm. The wavelength selection and Tikhonov regularization parameters were used to test the actual measured methane spectral data, and then we got that with other gas components the max cross sensitivity was 11.153 7%, the minimum cross sensitivity was 1.239 7%, and the root mean square prediction error was 0.004 8. The Tikhonov regularization algorithm effectively enhanced the accuracy in the light alkane mixed gas quantitative analysis. The feasibility of alkane gases mixture Fourier transform infrared spectrum wavelength selection was preliminarily verified by using the Tikhonov regularization algorithm.