Application And Development Of Chemometrics Methods In The Interpretation Of Remote Sensing FTIR Spectra

Techniques were set up in the dissertation with the aid of chemometrics, which were used for interpretation on remote sensing FTIR spectrum of VOCs (volatile organic compounds) in the atmosphere. These especially strengthen the advantage of remote sensing FTIR in quick, accurate, real-time and simultaneous determination of multi-component analysis. Since the remote sensing FTIR spectrum containing much noise, unknown interferents and background shift, strategies were put forward in the viewpoint of model robust and popularity. Based on the multivariate calibration method PLS (partial least squares), new signal correction method OSC (orthogonal signal correction) was applied to the filtration of noise or other unrelated parts and good performance was yielded in model simplicity and robust. Furthermore, PLS were improved in the direction of inner relation with the use of PPLS (polynomial partial least squares) model and were employed in the five-component mixture successfully. Calibration transfer was applied innovatively in the analysis of the remote sensing FTIR spectrum. With the optimization of this method, remote sensing FTIR spectra were analyzed directly with EPA spectra. PLS was also used for the pattern recognition of VOCs, and qualitative and quantitative analysis were achieved simultaneously with the help of experiment design. Primary interpretation of passive remote sensing FTIR spectrum was carried out. The innovation-application of PARAFAC (parallel factor analysis) was utilized for the analysis of passive remote sensing FTIR spectrum and qualitative and quantitative analyses were realized. The main conclusions were achieved as following:1. Interpretation on Remote Sensing FTIR Spectrum Based on PLSAccording to the signal characteristics of remote sensing FTIR, PLS was improved in the viewpoint of model popularity and robust. As for the remote sensing FTIR data, noise in the data enlarged the LVs (latent variables) of PLS and made it difficult for the separation of noise and information. OSC-PLS modified this situation in prediction accuracy and fewer latent variables and efficient filtration, which was validated by the complex system. Compared with PLS, the inner relation of PPLS was nonlinear, which was used successfully for the prediction of five-component system. Variables of PLS was optimized by GA (genetic algorithm) and then made the model simple and accuracy. However, this method was only suitable for the prediction of remote sensing data with remote sensing data itself. With the data compressed by PLS, the size of ANN (artificial neural network) input was smaller and prediction accuracy...