[Exploratory research on quantitative analysis of gaseous mixtures by AOTF-NIR spectrometer]

Due to its many advantages, such as miniaturization, high accuracy, high resolution, fast scanning speed, increased robustness and good stability, acousto-optic tunable filter (AOTF)-near infrared (NIR) spectrometer has been successfully applied in many fields. However, up to now, the commercial AOTF-NIR spectrometers can only be used for liquid and solid detection, but not for the detection of gaseous samples. In the present paper, the feasibility of quantitative analysis of gaseous mixtures by using AOTF-NIR spectrometer was investigated. A homemade gas cell was assembled to an AOTF-NIR spectrometer with probe for liquid detection to obtain NIR spectra of detected gas samples. The gas samples were composed of two groups: single-component CH4 and ternary component gaseous mixture of CH4, C2 H6, and C3 H8. The detection ability of fitted AOTF-NIR spectrometer was tested firstly. Comparing the absorption spectra of various concentrations, the absorbance of CH4 in absorption bands obviously increased with concentration increasing when the concentration was over 0.1%. According to the detection results, the lower limit of detection (LLD) of the AOTF-NIR spectrometer with gas cell was estimated to be 898 microL x L(-1). Subsequently, the NIR spectra of ternary mixtures were collected. The kernel partial least squares (KPLS) regression was employed to create the quantitative analysis model of three components gases. To evaluate the analysis ability of KPLS model, the PLS model was also created. The prediction results of the identical testing set show that the root mean square error of prediction (RMSEP) of three components predicted by KPLS model was 1.08%, 0.87%, and 0.79%, respectively, less than the RMSEP by PLS model. The exploratory work indicates that accurate quantitative analysis of ternary component alkane gaseous mixtures can be achieved by fitted AOTF-NIR spectrometer despite of some limitations, and KPLS regression is an excellent approach to NIR spectra analysis.