‘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. Practical considerations, interpretations and troubleshooting are emphasized and literature surveys are included to guide the reader to further work. The same dataset is used for all chapters dealing with calibration to demonstrate the differences between the different methodologies. Readers will learn how to handle spectral and chemical interferences in atomic spectrometry in a new, more efficient and cost-effective way.’
Table of Content
A GENERAL OVERVIEW OF ATOMIC SPECTROMETRIC TECHNIQUES IMPLEMENTING A ROBUST METHODOLOGY: EXPERIMENTAL DESIGNGS AND OPTIMIZATION ORDINARY MULTIPLE LINEAR REGRESSION AND PRINCIPAL COMPONENTS REGRESSION PARTIAL LEAST SQUARES REGRESSION REGRESSION USING ARTIFICIAL NEURAL NETWORKS
About the author
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).
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