Spectral simulation methods for enhancing qualitative and quantitative analyses based on infrared spectroscopy and quantitative calibration methods for passive infrared remote sensing of volatile organic compounds.

摘要

Infrared spectroscopy (IR) has over the years found a myriad of applications including passive environmental remote sensing of toxic pollutants and the development of a blood glucose sensor. In this dissertation, capabilities of both these applications are further enhanced with data analysis strategies employing digital signal processing and novel simulation approaches.; Both quantitative and qualitative determinations of volatile organic compounds are investigated in the passive IR remote sensing research described in this dissertation. In the quantitative work, partial least-squares (PLS) regression analysis is used to generate multivariate calibration models for passive Fourier transform IR remote sensing measurements of open-air generated vapors of ethanol in the presence methanol as an interfering species. A step-wise co-addition scheme coupled with a digital filtering approach is used to attenuate the effects of variation in optical path length or plume width.; For the qualitative study, an IR imaging line scanner is used to acquire remote sensing data in both spatial and spectral domains. This technology is capable of not only identifying but also specifying the location of the sample under investigation. Successful implementation of this methodology is hampered by the huge costs incurred to conduct these experiments and the impracticality of acquiring large amounts of representative training data. To address this problem, a novel simulation approach is developed that generates training data based on synthetic analyte-active and measured analyte-inactive data. Subsequently, automated pattern classifiers are generated using piecewise linear discriminant analysis to predict the presence of the analyte signature in measured imaging data acquired in remote sensing applications.; Near infrared glucose determinations based on the region of 5000--4000 cm-1 is the focus of the research in the latter part of this dissertation. A six-component aqueous matrix of glucose in the presence of five other interferent species, all spanning physiological levels, is analyzed quantitatively. Multivariate PLS regression analysis in conjunction with samples designated into a calibration set is used to formulate models for predicting glucose concentrations. Variations in the instrumental response caused by drift and environmental factors are observed to degrade the performance of these models. As a remedy, a model updating approach based on spectral simulation is developed that is highly successful in eliminating the adverse effects of non-chemical variations.