Determination of the Gas Density in Binary Gas Mixtures Using Multivariate Data Analysis

Abstract

Some solvents in commercial products may have harmful effects on human health. It is important to determine the percentage of this certain solvent in a product to detect any possible health hazards. In this paper, three different solvents, acetone, methanol, and chloroform, are used to form binary gas mixtures in a laboratory environment. Nine quartz-crystal microbalance sensors are used, and gas data are obtained through the responses of these sensors. First, the data set divided 11 times randomly for validation sensitivity of the results. For each of the binary gas mixtures, insignificant sensors are removed, considering multivariate analysis of variance analysis, and sensor data sets are obtained. The statistical multivariate linear regression (MvLR) method is used to determine the ratio of individual gasses in each binary gas mixture. Flexible models are created by removing insignificant sensor data from the equations in the MvLR. Prediction performances of 11 data sets reveal and validate that statistical methods can be used to detect the ratio of a certain gas within a gas mixture, and reliable results can be achieved.