Retrieving CO concentrations from FT-IR spectra with nonmodeled interferences and fluctuating baselines using PCR model parameters

It is demonstrated that good predictions of gas concentrations based on mea sured spectra can be made even if these spectra contain totally overlapping spectral features from nonidentified and non-modeled interfering compounds and fluctuating baselines. Tile prediction program (CONTOUR) is based sole ly on principal component regression (PCR) model parameters, CONTOUR consis ts of two smaller algorithms. The first of these is used to calculate pure component spectra based on the PCR model parameters at different concentrat ions. In the second algorithm, the calculated pure component spectra are su btracted one by one from the contaminated spectrum, and the length of the s pectral contour within specified wavenumbers is then calculated. When the l ength of the contour is at a minimum, a condition is reached where the pure component part of the measured spectrum is absent and only the background signal remains. The assumptions are that the background and analytical sign als must be additive and that no accidental match between these signals tak es place. The best results are obtained with the use of spectra with a high selectivity. The use of the program is demonstrated hg applying simple sin gle-factor PCR models based on pure gaseous 1 and 4 cm(-1) CO Fourier trans form infrared (FT-IR) spectra (50-400 ppm) measured at ambient temperatures . The program is validated with measured CO spectra containing interferents such as N2O, CO2, and added Hitran-simulated H2O, CO2, and COS spectra, re presenting strong features in the CO spectral region.