Comparison between partial least square and support vector regression with a genetic algorithm wavelength selection method for the simultaneous determination of some oxygenate compounds in gasoline by FTIR spectroscopy

In the current research, FTIR spectroscopy (Mid, 600-4000 cm(-1)) coupled with a multivariate calibration method has been suggested as a powerful regression model for the simultaneous determination of oxygenate in gasoline. To reach that goal, partial least squares regression (PLS-R) combined with genetic algorithm wavelength selection method (GA) was compared with the GA- support vector regression (GA-SVR) method. In order to evaluate the models, root mean square error of prediction, and leave-one-out cross-validation root mean square error, as well as the correlation coefficient between the calculated (R-cal(2)) and predicted values (R-pred(2)), were applied. Based on the findings in this work, GA-SVR model is the superior predictive factor of the two, having a higherR(pred)(2) (0.971, 0.950, 0.955, 0.960, 0.970, and 0.969) and a lower root mean square error of prediction values (RMSEP = 0.185, 0.245, 0.218, 0.229, 0.218, and 0.227) respectively for methyl t-butyl ether (MTBE), iso-butanol, n-butanol, propanol, ethanol, and methanol in comparison to PLS (R-pred(2) = 0.951, 0.940, 0.938, 0.940, 0.952, and 0.949; RMSEP = 0.32, 0.283, 0.303, 0.299, 0.300, and 0.311). The lowest detection limit was 0.06% w/w for GA-SVR and 0.2% w/w for GA-PLS model. Also, in a concentration range from 0.06 to 3.5% w/w the values were in accordance to gas chromatography analysis of oxygenates compound. Hence, together with GA-SVR, FTIR can be an efficient, real-time approach towards a feasible quantitative analysis of oxygenate compounds in gasoline.