Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.636067
Title: Industrial applications of remote and in-situ laser induced breakdown spectroscopy
Author: Beddows, D. C. S.
Awarding Body: University of Wales Swansea
Current Institution: Swansea University
Date of Award: 2000
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Abstract:
The major objectives of the work reported in this thesis was the development of spectrum collection and analysis procedures for potential industrial applications of Laser Induced Breakdown Spectroscopy, using either a single-fibre probe or a telescopic "point-and-shoot" arrangement. Traditionally, only univariate methods (spectral line intensity vs elemental concentration) have been considered for data analysis in such applications. The univariate approach was repeated here but then replaced with more sophisticated multivariate analysis algorithms. They are well established in e.g. Raman and FTIR spectroscopy, but never before have they been used in LIBS. By using the multivariate Partial Least Squares (PLS) algorithm the improvements made in measurement precision and accuracy have been tested by analysing a range of trace elements (C, Si, Cr, Co, Nb, P and B) in steels. The analysis of these elements is often difficult due to peak interference of matrix lines with the trace element emissions. Not only can the proposed technique deal with peak interference and remove spectral noise from the calibration models, in addition it provides statistical means for the user to assess whether a particular selected calibration model is suitable for the analysis of an "unknown" sample. These measurements thus help the operator to avoid frequently encountered problems associated with matrix effects. Where accurate elemental concentrations are not needed, Discriminant analysis can be applied, to compare and match spectra of "unknown" samples to library spectra of calibration samples. This type of "qualitative" analysis has been used here for remote material identification and sorting. Materials of different matrix materials (e.g. Al, Cu, Pb, Zn, glass, mild steel and stainless steel) could be identified by the Mahalanobis Distance algorithm with 100% certainty, and even materials of similar matrix materials (e.g. grades of steels) could be differentiated reliably.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.636067  DOI: Not available
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