直接正交信号校正算法在烷烃类多组分气体定量分析中的应用

Application of Direct Orthogonal Signal Correction Algorithm in Multi-Component Alkane Quantitative Analysis

针对烷烃类多组分混合气体中红外光谱存在的基线漂移问题,提出一种直接正交信号校正算法用于光谱数据预处理。实验中采用傅里叶变换红外光谱仪采集了936组混合气体样本的光谱数据,混合气体主要由不同浓度范围的七种组分气体组成。将直接正交信号校正算法与导数算法进行了对比分析,采用偏最小二乘回归方法建立了各组分气体定量分析模型,并对模型参数(主元个数、导数步长及正交分量的个数)进行了遍历优化选取最优分析模型。结果表明直接正交信号校正算法对于中红外光谱基线校正效果最好,直接正交信号校正算法用于烷烃类混合气体中红外光谱基线校正可行,效果良好,具有一定的实用和研究价值。

According to the baseline departure of multicomponent alkane gas mixture spectra, direct orthogonal signal correction （DOSC） algorithm was proposed to pretreat the infrared spectra data. Fourier transform infrared （FTIR） spectrometer was used to sample 936 spectra data of seven components gas mixture, including methane, ethane, propane, isobutane, nbutane, iso pentane and npentane gases. The concentration of each component ranges from 0.01% to 0. 1%, 0. 01% to 0. 1%, 0. 01% to 0. 15%, 0. 0% to 0. 1%, 0. 0% to 0. 1%, 0. 0% to 0. 05%, and 0.0% to 0. 05%, respectively. For analyzing intuitively, par tial least square regression （PLSR） was introduced to build the component gas quantitative analysis model. In experiment, DOSC method was compared with first derivative algorithm （FDA） and second derivative algorithm （SDA）. In order to get the optimal model, ergodic optimization method was used to select the optimal parameters of the model, i.e. the step of the derivative algo rithm, the number of the primary component of the PLSR and the number of orthogonal components of the DOSC algorithm. The experiment results show that DOSC algorithm has the better effect in the field of infrared spectra data pretreating. The aver age mean relative error （MRE） of the component gas analysis models is 16.58%, which declined by 66.80% from the average MRE before data pretreating 49.93%. Compared with DA, the average MRE declined by 51.51% from 34.19% after pretreated by FDA, and declined by 56.30% from 37.94% after pretreated by SDA. So DOSC method is feasible to pretreat the IR spectra data, and has definite practical and investigation value.