Chemometrics and infrared emission spectroscopy for remote analysis

Abstract

Chemometrics is a discipline of chemistry that uses mathematical and statistical techniques to extract useful information from chemical data. Infrared emission spectroscopy is one of a very few analytical techniques that allows noninvasive and remote measurements. This dissertation uses the chemometric methods of multivariate calibration and multiresponse nonlinear optimization to extract useful information from infrared emission data. Partial Least Squares and Locally Weighted Regression are used for prediction of chemical and physical properties of a free standing polymer system and a polymer coated aluminum system. Multiresponse nonlinear optimization is used to model the dynamic process of a polymer curing reaction. The choice of the proper objective function for multiresponse nonlinear optimization is also investigated.