Basic Chemometric Techniques In Atomic Spectroscopy
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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.
Andrade-Garda, Jose: - Dr in Chemistry, 1995. Since 1999 he is an Associated Professor at the Department of Analytical Chemistry, University of A Coruña. Fields of expertise: Quality Control and Chemometrics. In Chemometrics, main interests are in multivariate regression and pattern recognition methods (either, unsupervised and supervised). In the Atomic Spectrometry arena, he applied formal optimisation techniques to optimize analytical protocols (experimental design and Nelder and Mead simplex) and applied multivariate regression tools to cope with spectral and chemical interferences in ETAAS (traditional sample introduction and slurry-based).