Basic Chemometric Techniques in Atomic Spectroscopy: (Volume 13 RSC Analytical Spectroscopy Series)

The first edition of this book was a 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 Contents:
An overview of atomic spectrometric techniques; Classical linear regression by the least squares method; Implenting a robust methodology: experimental designs and optimisation; Ordinary multiple linear regression and principal components regression; Partial least-squares regression; Multivariate regression using artificial neural networks and support vector mechines; Index

About the Author :
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.

Review :
"The application of statistical and chemometric tools for planning, executing and interpreting analytical measurements is a common thread in the current practice of analytical chemistry. This book fits nicely into the contemporary picture, addressing some of the training needs of analytical chemists in a subject area that is still neglected in academic courses." "The chapters are uniformly good, and although written by multiple authors, are coherent, focused and remain on message with limited overlap. " "The level of presentation is suitable for first year graduate students and professionals in industry with a strong scientific background" "In conclusion, this is a well-organized and concise text, which I can recommend to those wishing to explore data analysis techniques, especially for calibration, to gain insights into correct procedures and to avoid common mistakes. The text is very readable and will reward those of all abilities who delve into its contents." -- Colin F. Poole Chromatographia (2015) 78:451-452 The application of statistical and chemometric tools for planning, executing and interpreting analytical measurements is a common thread in the current practice of analytical chemistry. This book fits nicely into the contemporary picture, addressing some of the training needs of analytical chemists in a subject area that is still neglected in academic courses. There are two general features of this book that should be stated at the onset. This is a book about chemometrics and not atomic spectroscopy. A fairly general and broad description of atomic spectroscopy is given in the first chapter but the bulk of the material in this book would apply to almost any analytical technique. The authors of individual chapters use examples taken mainly from atomic spectroscopy, but these are often only minor incursions. The second feature is that the book is organized around procedures for univariate and multivariate calibration with a comprehensive, although quite general, chapter on experimental design. Other chemometric approaches for quality assurance and classification of data are only briefly touched upon. The latter is perhaps the most obvious omission, since atomic spectroscopic methods are capable of generating a large amount of chemical information and its organization for interpretation by data reduction and pattern recognition methods would be of some interest to readers attracted to this book by its title. The central theme of this book is calibration with individual chapters covering least means squares regression, multiple linear regression, principal component regression, partial least squares regression, artificial neural networks, and support vector machines. The chapters are uniformly good, and although written by multiple authors, are coherent, focused and remain on message with limited overlap. Mathematical descriptions are unavoidable in chemometrics, but the authors have made a conscious effort to explain processes and techniques in words, such that the equations and matrix algebra do not get in the way of understanding how to use the tools described. The level of presentation is suitable for first year graduate students and professionals in industry with a strong scientific background, if only a casual acquaintance with chemometrics. The main topic chapters are broad enough that anyone with an interest in calibration would benefit from reading the text, even if they had no intention to apply atomic spectroscopy in their work. Here, there is overlap and competition with other introductory texts on data analysis and chemometrics. This book fares well in such comparisons. Two components of the book I am not sure are necessary but can be tolerated. The chapter on experimental design and optimization contains two long tables that occupy 54 pages (not including their associated references) which simply list sample information and briefly describe the type of experimental design used in cited reports where atomic spectroscopy was used as the determination step. This is not particularly insightful as it is not integrated into the chapter discussion with many of the procedures focused on sample preparation and not instrumental aspects. The chapter on experimental design should not be singled out as in some way weak, since it is not; it also includes in the section describing Simplex optimization, lengthy sections of "pseudo-code" for deploying the Simplex algorithm. The Simplex algorithm is included in many software packages and these incomplete descriptions of the program language would seem to be of interest only to those capable of writing software for data analysis. It is likely that most readers of this book would be more interested in how to select and perform Simplex methods in a meaningful way, and would access operational aspects through commercial software, rather than create their own software for these procedures. I would have preferred to have the space consumed in the above way utilized to add information on classification methods for data reduction and pattern recognition. In conclusion, this is a well-organized and concise text, which I can recommend to those wishing to explore data analysis techniques, especially for calibration, to gain insights into correct procedures and to avoid common mistakes. The text is very readable and will reward those of all abilities who delve into its contents. -- Colin F. Poole Chromatographia (2015) 78:451-452