DOI
https://doi.org/10.25772/C6G4-7091
Defense Date
2016
Document Type
Dissertation
Degree Name
Doctor of Philosophy
Department
Chemistry
First Advisor
Sarah C Rutan
Abstract
In chemical analyses, it is crucial to distinguish between chemical species. This is often accomplished via chromatographic separations. These separations are often pushed to their limits in terms of the number of analytes that can be sufficiently resolved from one another, particularly when a quantitative analysis of these compounds is needed. Very often, complicated methods or new technology is required to provide adequate separation of samples arising from a variety of fields such as metabolomics, environmental science, food analysis, etc.
An often overlooked means for improving analysis is the use of chemometric data analysis techniques. Particularly, the use of chemometric curve resolution techniques can mathematically resolve analyte signals that may be overlapped in the instrumental data. The use of chemometric techniques facilitates quantitation, pattern recognition, or any other desired analyses. Unfortunately, these methods have seen little use outside of traditionally chemometrics focused research groups. In this dissertation, we attempt to show the utility of one of these methods, multivariate curve resolution-alternating least squares (MCR-ALS), to liquid chromatography as well as its application to more advanced separation techniques.
First, a general characterization of the performance of MCR-ALS for the analysis of liquid chromatography-diode array detection (LC-DAD) data is accomplished. It is shown that under a wide range of conditions (low chromatographic resolution, low signal-to-noise, and high similarity between analyte spectra), MCR-ALS is able to increase the number of quantitatively analyzable peaks. This increase is up to five-fold in many cases.
Second, a novel methodology for MCR-ALS analysis of comprehensive two-dimensional liquid chromatography (LC x LC) is described. This method, called two dimensional assisted liquid chromatography (2DALC), aims to improve quantitation in LC x LC by combining the advantages of both one-dimensional and two dimensional chromatographic data. We show that 2DALC can provide superior quantitation to both LC x LC and one dimensional LC under certain conditions.
Finally, we apply MCR-ALS to an LC x LC analysis of fourteen furanocoumarins in three apiaceous vegetables. The optimal implementation of MCR-ALS and subsequent integration was determined. For these data, simply performing MCR-ALS on the two dimensional chromatogram and manually integrating the results proved to be the superior method. These results demonstrate the usefulness of these curve resolution techniques as a compliment to advanced chromatographic techniques.
Rights
© The Author
Is Part Of
VCU University Archives
Is Part Of
VCU Theses and Dissertations
Date of Submission
6-27-2016