Basic Chemometric Techniques in Atomic Spectroscopy

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Jose Manuel Andrade-Garda
Royal Society of Chemistry, Jun 15, 2009 - Mathematics - 300 pages
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This is the first book for atomic spectroscopists to present the basic principles of experimental designs, optimization and multivariate regression. Multivariate regression is a valuable statistical method for handling complex problems (such as spectral and chemical interferences) which arise during atomic spectrometry. However, the technique is underused as most spectroscopists do not have time to study the often complex literature on the subject. This practical introduction uses conceptual explanations and worked examples to give readers a clear understanding of the technique. Mathematics is kept to a minimum but, when required, is kept at a basic level.

 

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Contents

A General Overview of Atomic Spectrometric Techniques
1
References
47
Experimental Design
51
Ordinary Multiple Linear Regression and Principal
160
Partial LeastSquares Regression
181
References
236
Multivariate Regression using Artificial Neural Networks
244
References
276
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About the author (2009)

Jose Andrade-Garda is based in the Department of Analytical Chemistry at the University of A Coru a where he specializes in quality control and chemometrics. Within the field of chemometrics, his main interests are multivariate regression and pattern recognition methods. In the atomic spectrometry arena, he has applied formal optimization techniques to optimize analytical protocols and used multivariate regression tools to cope with spectral and chemical interferences in ETAAS.

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