Comparison of multivariate calibration methods for quantitative analysis of multicomponent mixture of air toxic organic compounds by FTIR

The quantitative prediction abilities of three competing multivariate calibration methods for concentration analysis of FTIR Spectra are compared. The calibration methods compared include classical least squares method (CLS), Kalman filter method (KFM) and partial least squares method (PLS). The mixtures of seven air toxic organic compounds whose FTIR Spectra are known to seriously overlap were chosen to evaluate the preceding calibration methods. The concentrations of the seven air toxic organic compounds mixtures varied from 1 to 50 or 100 ppm. A relatively simple model involving the mean prediction error (MPE) and mean relative error (MRE) was developed for estimating each calibration method mentioned above. The results showed that PLS is the best calibration method among the three methods examined for a given real spectral data set while CLS and KFM had no obvious difference in the performance. Better predictable results were obtained when the measurement is taken at a series of equispaced wavenumbers of the absorption band for the desired component.