The Qualitative And Quantitative Study Of Complex Multicomponent System With Chemometrics And Remote Sensing FTIR Technology

Qualitative and quantitative analysis of VOCs (Volatile Organic Compounds)in the atmosphere are carried out in combination with remote sensing FTIR(Fourier transform infrared spectrum) technology and chemometrics methods inthis dissertation. The main contents are as follows:1. Interpretation on Remote Sensing FTIR Spectrum Based on ChemometricsIn consideration of ANN's weakness of excess training time and overfittingmodels, this dissertation adopts PLS and PCA to extract principal component fromANN spectrum data, and uses GA to choose wavelength to simplify models, toeliminate irrelevant variables, and to enhance analytic speed. Four models,PLS-BP-ANN, PCA-BP-ANN, GA-BP-ANN and BP-ANN, are built to performsimultaneous quantitative analysis of multicomponents in the mixture when thereare serious overlapping between the spectral bands of the compounds Comparingthe predictive error of the four methods with the quantitative measurement for thefive-component atmosphere VOCs(benzene, toluene, methanol, chloroform andacetone), PLS-BP-ANN model had the best robustness.2. Calibration Transfer for the Interpretation on Remote Sensing FTIRSpectrumThe idea of calibration transfer is introduced into the analysis of remotesensing FTIR spectrum this dissertation. Calibration transfer means to predictsuccessfully the signals of other uniform instrument with the calibrated modelbuilt with one instrument. As for the data of remote sensing FTIR, it is regarded asthe deviation from EPA data in this research. And then the method of PA(Procrustes Analysis) is adopted to get rid of discrepancy among instrumentswhile its main idea is to eliminate the parts in X (such as absorbance) irrelevant toY (such as concentration). PA focuses on the process of metrical signals ofdifferent apparatus to get rid of discrepancy among them and maintains bettersteadiness. Hence, it is a more significant method of calibration transfer. Thisresearch adopts the method of PA to realize the transfer of BP-ANN model madeup of acetone, benzene, chloroform and methanol between two FTIR apparatus. Ityields high prediction accuracy and fulfills the prediction of remote sensing data with EPA data.3. Classification of VOCs in the Atmosphere Based on Artificial NeuralNetworkConsidering the confinements of slow speed of study convergence, too muchlayers of network and local optimization with BP-ANN model, the improved ANNmodels, PNN(probabilistic neural network) and LVQ(learning vector quantization)which are suitable to pattern classification are built in this dissertation. For thesix-component system chloroform, methanol, acetone, hexane, toluene andmethyene chloride in the atmosphere, the qualitative measurement performance ofPNN, LVQ, and BP-ANN are compared while the method of PNN maintains thebest classification accuracy with 93.3%. Besides, PNN is characterized of simplestructure and fast training speed. And the new training samples are easier to addinto the former trained classifiers for PNN model. Consequently, PNN is suitablefor the real-time and on-line qualitative monitoring of atmospheric environmentand can perform environmental alarm.4. Pattern Recognition of Remote Sensing FTIR SpectrumThe method of PCA-LDA (principal components analysis-linear discriminantanalysis) is introduced creatively in the analysis of remote sensing FTIR spectrumin this dissertation and the method of pattern recognition of PLS-LDA andPCA-LDA are built to recognize VOCs in the atmosphere. In the qualitativedifferentiation of five components with overlapped spectrum, that is, hexane,benzene, toluene, acetone and methyene chloride, both methods have high ratio ofrecognition and PLS-LDA yields a little higher one than PCA-LDA. This researchis of great significance to the control of pollution in our life. This method is alsopromoted to qualitative analysis of other complex system in this research such asthe classification of carcinogenicity of PAHs (polycyclic aromatic hydrocarbons).When the carcinogenicity is classified on high, low, and no level, the predictiveaccuracy reaches 100%.