Statistics and Chemometrics for Analytical Chemistry

by Madjackfrost on October 10, 2009

This book, 5th Ed, will benefit analytical scientists who wish to design and conduct their experiments properly, and extract as much information from the results as they legitimately can. It is intended to be of value to the rapidly growing number of students specializing in analytical chemistry, and to those who use analytical methods routinely in everyday laboratory work.

There are two further and related reasons that have encouraged authors to write this book. One is the enormous impact of microelectronics, in the form of microcomputers and hand-held calculators, on statistics: these devices have brought lengthy or difficult statistical procedures within the reach of all practising scientists. The second is the rapid development of new ‘chemometric’ procedures, including pattern recognition, optimization, numerical filter techniques, simulations and so on, all of them made practicable by improved computing facilities. The last chapter of this book attempts to give the reader at least a flavour of the potential of some of these newer statistical methods. Unfortunately they didn’t include any kind of information regarding computer programs in this book.

To limit the length of the book, and to emphasize its practical bias, authors have made no attempt to describe in detail the theoretical background of the statistical tests described. But they have tried to make it clear to the practising analyst which tests are appropriate to the types of problem likely to be encountered in the laboratory. There are worked examples in the text, and exercises for the reader at the end of each chapter.

Overview of Chapters of this 5th edition:

The main areas where new material has appeared in this edition are in Chapters 4–8. Chapter 4 includes an expanded treatment of control charts and additional material on uncertainty and on proficiency testing schemes. In Chapter 5 there is more material on the use of regression lines for method comparisons. Chapter 6 reflects the continuing growth of importance of robust methods, and Chapter 7 provides extra sections on factorial designs and on the simplex optimization method. The use of multivariate methods is now very common, so Chapter 8 includes an extended discussion of the principal components and partial least squares regression methods, and more on neural networks. In the earlier chapters on basic statistics the main changes are the greater emphasis on the Grubbs outlier test and a move of the section of Kolmogorov methods to Chapter 3. The bibliographies for each chapter have been updated, with rather more annotations than in the past, and with more emphasis on publications from standards organizations.

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