Multivariate methods have been developed to assist in the interpretation of multiwavelength spectral data. When carrying out an elemental analysis by optical emission spectrometry, the analyst is faced with a choice of many spectral lines on which to base the analysis. The problem of choosing a suitable line depends upon other components in the sample, as some of these lines may suffer from spectral interferences, depending upon the nature of the sample matrix. The selection of the most suitable line for the determination of a desired component is conventionally accomplished by consulting tables of spectral emissions, and selecting a line on the basis of freedom from spectral overlap, taking into consideration the bandwidth of the spectrometer. Unfortunately, many weaker lines are not tabulated, although they may nevertheless interfere if the interfering element is at a high enough concentration. As well, emissions from sample-specific species, such as molecular species, will not be tabulated. As the possibility that a spectral line will cause a significant interference depends upon interferent concentration, the analyst has previously required detailed knowledge of the nature of other components in a sample.
The methods developed in this thesis facilitate the determination of a desired component in binary, ternary, or more complex mixtures without prior knowledge of the nature of any interfering components. This automatic line selection allows the matrix-dependent tailoring of the lines chosen to the element or elements of interest. Factor analysis determines those wavelengths where an unidentified interferent contributes to the measured intensity. A multivariate analysis based on selected wavelengths gives the concentrations of all desired components while avoiding error in these concentrations due to interferents.
Using a related method, the best analytical line for the determination of a specified analyte is selected from a set of lines on the basis of the least interference in a particular sample matrix, by several cycles of mathematical analysis. Unsuitable lines are rejected in the first few cycles, the best lines being retained until last. A multivariate analysis after each cycle provides an updated estimate of the analyte concentration. As with other methods in this thesis, this process is performed without reference to spectral tables.
Application of these methods to a recently developed high resolution photodiode array based polychromator system is also discussed. This has consequences for the design and selection of spectral masks to assist in multielement analysis on this spectrometer.
In the course of development of these multivariate methods, the need for improved dynamic range in the photodiode array was seen. An algorithm for the generation of dynamic range enhanced photodiode array spectra has been developed and implemented. / Science, Faculty of / Chemistry, Department of / Graduate
| Identifer | oai:union.ndltd.org:UBC/oai:circle.library.ubc.ca:2429/31418 |
| Date | January 1990 |
| Creators | Wirsz, Douglas Franklin |
| Publisher | University of British Columbia |
| Source Sets | University of British Columbia |
| Language | English |
| Detected Language | English |
| Type | Text, Thesis/Dissertation |
| Rights | For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use. |
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