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dc.contributor.advisorDenton, M. Bonneren_US
dc.contributor.authorMadden, Sean Paul
dc.creatorMadden, Sean Paulen_US
dc.date.accessioned2013-04-25T09:54:26Z
dc.date.available2013-04-25T09:54:26Z
dc.date.issued1999en_US
dc.identifier.urihttp://hdl.handle.net/10150/284073
dc.description.abstractAs the amount of data generated by today's advanced analytical instrumentation grows, the need for more intelligent and strategic manipulation of that data increases correspondingly. In the case of echelle ICP-AES employing array detectors, the large amount of the data available in a given experiment is often under-utilized. Efficient utilization of the vast information content available in these spectra holds promise for significant improvements in elemental analysis. Many samples, especially those that are completely unknown, require the expertise and time of a highly trained user in order to develop a robust and reliable method that will also be appropriate for the further analysis of similar samples. More intelligent use of the available data and implementation of multivariate chemometric techniques can afford improvement and even automatic generation of analytical methods for atomic emission spectroscopy. An intelligent multivariate calibration protocol has been developed, which is suitable for use with data from any of the state-of-the-art echelle-CID based atomic emission instruments, including those employing ICP, DCP, Arc, or spark sources. Protocols are based on custom approaches to direct and iterative classical least squares (CLS) fitting of signal vectors to stored calibration matrices. Subsequent to calibration, rapid semi-quantitation is made possible, allowing for more strategic choices of standards, more systematic selection of lines for quantitation, improved identification, better interference correction, and ultimately more precise and accurate quantitation and higher sample throughput. Practical demonstration is presented with both validation and unknown sample sets, using ICP-AES. The calibration protocol developed demonstrates the feasibility of a stored, stable, and correctable multivariate calibration matrix. State-of-the-art echelle-CID based ICP-AES instrumentation has been under-utilized as a method of simultaneous multielement-specific detection in chromatographic applications due to shortcomings in the acquisition and manipulation of data. New software to facilitate export, viewing, and calculations with time-resolved ICP-AES data has been developed. Applications are presented for the speciation of compounds of toxicological importance, including potential arsenic metabolites, a proposed biological model arsenic-selenium complex of glutathione, and a novel mercury selenium glutathione complex.
dc.language.isoen_USen_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.subjectChemistry, Analytical.en_US
dc.titleMethodology advancements for multivariate calibration and elemental speciation with echelle-CID based ICP-AESen_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.leveldoctoralen_US
dc.identifier.proquest9960276en_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineChemistryen_US
thesis.degree.namePh.D.en_US
dc.identifier.bibrecord.b40273581en_US
refterms.dateFOA2018-06-16T11:45:57Z
html.description.abstractAs the amount of data generated by today's advanced analytical instrumentation grows, the need for more intelligent and strategic manipulation of that data increases correspondingly. In the case of echelle ICP-AES employing array detectors, the large amount of the data available in a given experiment is often under-utilized. Efficient utilization of the vast information content available in these spectra holds promise for significant improvements in elemental analysis. Many samples, especially those that are completely unknown, require the expertise and time of a highly trained user in order to develop a robust and reliable method that will also be appropriate for the further analysis of similar samples. More intelligent use of the available data and implementation of multivariate chemometric techniques can afford improvement and even automatic generation of analytical methods for atomic emission spectroscopy. An intelligent multivariate calibration protocol has been developed, which is suitable for use with data from any of the state-of-the-art echelle-CID based atomic emission instruments, including those employing ICP, DCP, Arc, or spark sources. Protocols are based on custom approaches to direct and iterative classical least squares (CLS) fitting of signal vectors to stored calibration matrices. Subsequent to calibration, rapid semi-quantitation is made possible, allowing for more strategic choices of standards, more systematic selection of lines for quantitation, improved identification, better interference correction, and ultimately more precise and accurate quantitation and higher sample throughput. Practical demonstration is presented with both validation and unknown sample sets, using ICP-AES. The calibration protocol developed demonstrates the feasibility of a stored, stable, and correctable multivariate calibration matrix. State-of-the-art echelle-CID based ICP-AES instrumentation has been under-utilized as a method of simultaneous multielement-specific detection in chromatographic applications due to shortcomings in the acquisition and manipulation of data. New software to facilitate export, viewing, and calculations with time-resolved ICP-AES data has been developed. Applications are presented for the speciation of compounds of toxicological importance, including potential arsenic metabolites, a proposed biological model arsenic-selenium complex of glutathione, and a novel mercury selenium glutathione complex.


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