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

According to the baseline departure of multi-component 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, iso-butane, n-butane, isopentane and n-pentane 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, partial 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 algorithm, 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 average mean relative error (MRE) of the component gas analysis models is 16. 58%, which declined by 66. 80% from the average MIRE before data pretreating 49. 93%. Compared with DA, the average MIRE 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.