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Application of artificial intelligence techniques for inductively coupled plasma spectrometry

The development of intelligent components for the automated analysis of samples by inductively coupled plasma (ICP) Spectrometry is presented. An expert system for diagnosing an ICP atomic emission spectrometry (AES) system using a blank solution was developed as a warning system. This expert system was able to warn the system of major malfunctions and was able to identify most problems. Three pattern recognition techniques were compared in their ability to recognize similar geological samples in small databases. Two of these techniques, k-Nearest Neighbours and Bayesian Classification, worked extremely well with over 96% success. The development of an objective function for multi-element optimizations in ICP-AES is presented. Various aspects of the application of a Simplex optimization were explored for the optimization of the ion optics of an ICP-mass spectrometry (MS) system. An algorithm for the automatic selection of internal standards for analytes in difficult samples in ICP-MS is presented.

Identiferoai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:QMM.35610
Date January 1998
CreatorsSartoros, Christine.
ContributorsSalin, Eric D. (advisor)
PublisherMcGill University
Source SetsLibrary and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada
LanguageEnglish
Detected LanguageEnglish
TypeElectronic Thesis or Dissertation
Formatapplication/pdf
CoverageDoctor of Philosophy (Department of Chemistry.)
RightsAll items in eScholarship@McGill are protected by copyright with all rights reserved unless otherwise indicated.
Relationalephsysno: 001641569, proquestno: NQ44574, Theses scanned by UMI/ProQuest.

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