[Principal component extraction used for the interpretation of RS-FTIR spectra]

A method for interpretation of remote sensing FTIR spectra was set up based on ANN model. Considering long training time and over-fitting problem of ANN, two methods, partial least squares (PLS) and principal component analysis (PCA), were utilized to extract principal components of spectra, process time decrease from about 30 minutes to a few seconds. Meanwhile, the idea of calibration transfer was used to overcome the limitation of calibration model in remote sensing FTIR spectra analysis. With the optimization of ANN model, four-component mixtures of acetone, benzene, chloroform and methanol were predicted in a remote sensing and real-time way while the calibration model was built with EPA data. The best performance was yielded with PLS-ANN model, and the root mean square error (RMSE) of acetone, benzene, chloroform and methanol were 0.043, 0.031, 0.034 and 0.051 respectively, which confirm the real-time, correct and quick analysis of remote sensing FTIR in air monitoring.