Chemometrics books

Heberger et al. [55] used principal component analysis (PCA) to reduce the amount of test solutes when calculating Flory-Huggins parameters x 2i- Subsequently, PCA became a popular technique in data analysis for pattern recognition and dimension reduction, as it can reveal several underlying components, and may also help to explain the vast majority of variance among the data [56,57]. PCA is particularly useful for classifying stationary phases [58,59], polarity [56], and interaction parameters [57]. Detailed descriptions of PCA are available in standard chemometric books and reviews [58,59]. Notably, the method should facilitate the solution of problems connected with the solute dependence of the x 2i parameter. 

An exhaustive list of all possible types of data suitable for chemometric treatment together with all possible types of predictions made from the data would fill a large chapter in this book. Table 1 contains a brief list of some of... 

Rousseeuw [4]. Massart and Kaufman [5] and Bratchell [6] wrote specifically for chemometricians. Massart and Kaufman s book contains many examples, relevant to chemometrics, including the meteorite example [7]. More recent examples concern classification, for instance according to structural descriptions for toxicity testing [8] or in connection with combinatorial chemistry [9], according to chemical... 

Many books have been published about pattern recognition one of these is directed towards chemometrics [1],... 

Advantages of these methods are that no a priori assumptions about distributions are necessary and that probabilistic decisions can be taken more easily than with -NN. In chemometrics, the method was introduced under the name ALLOC [17, 18]. The methodology was described in detail in a book by Coomans and Broeckaert [19]. The method was developed further by Forina and coworkers [20,21]. 

Much of the material presented in this book is based on the direct experience of the authors. This would not have been possible without the hard work and input of our colleagues, students and post-doctoral fellows. We sincerely want to acknowledge each of them for their good research and contributions without which we would not have been able to treat such a broad range of subjects. Some of them read chapters or helped in other ways. We also owe thanks to the chemometrics community and at the same time we have to offer apologies. We have had the opportunity of collaborating with many colleagues and we have profited from the research and publications of many others. Their ideas and work have made this book possible and necessary. The size of the book shows that they have been very productive. Even so, we have cited only a fraction of the literature and we have not included the more sophisticated work. Our wish was to consolidate and therefore to explain those methods that have become more or less accepted, also to... 

This list of abbreviations contains both acronyms which are generally used in analytical chemistry and such applied in the book. In addition to terms from analytical methods, essential statistical and chemometrical terms as well as acronyms of institutions and organizations are included. Terms of very particular interest are explained on that spot. 

After discussing this between ourselves, we decided that we have reached a point where it is worthwhile to present our readers with a complete set of the chemometrics writings published to date. Those of you who have been reading our work for a long time will recall that the column series Chemometrics in Spectroscopy is a continuation of our previous column series, Statistics in Spectroscopy . Statistics in Spectroscopy was published from 1986 to 1992, with some preliminary articles in 1985. The columns from the earlier series, Statistics in Spectroscopy , have been collected and published in their entirety as a book (with minor editorial changes appropriate to the change in format from a series of columns to a book) of the same name, now in its second edition. So much for the past what about the discussion ... 

I know of no experienced practitioner of chemometrics who would blindly use the full spectrum when applying PLS or PCR. In the book Chemometrics by Beebe, Pell and Seasholtz, the first step they suggest is to examine the data. Likewise, Kramer in his new book has two essential conditions The data must have information content and the information in the data must have some relationship with the property or properties which we are trying to predict. Likewise, in the course I teach at Union Carbide, I begin by saying that no modeling technique, no matter how complex, can produce good predictions from bad data. ... 

We have been writing about statistics and chemometrics for a long time. Long-time readers of the column series published in Spectroscopy magazine will recall that the series name changed since its inception. The original name was Statistics in Spectroscopy (which was a multiple pun, since it referred to Statistics in Spectroscopy and Statistics in Spectroscopy as well as statistics (the subject of Statistics) in Spectroscopy (see our third column ever [1] for a discussion of the double meaning of the word Statistics . The same discussion is found in the book based on those first 38 columns in the earlier Statistics series [2])). 

Advanced mathematical and statistical techniques used in analytical chemistry are often referred to under the umbrella term of chemometrics. This is a loose definition, and chemometrics are not readily distinguished from the more rudimentary techniques discussed in the earlier parts of this chapter, except in terms of sophistication. The techniques are applied to the development and assessment of analytical methods as well as to the assessment and interpretation of results. Once the province of the mathematician, the computational powers of the personal computer now make such techniques routinely accessible to analysts. Hence, although it would be inappropriate to consider the detail of the methods in a book at this level, it is nevertheless important to introduce some of the salient features to give an indication of their value. Two important applications in analytical chemistry are in method optimization and pattern recognition of results. 

Principal Component Regression, PCR, and Partial Least Squares, PLS, are the most widely known and applied chemometrics methods. This is particularly the case for PLS, for which there is a tremendous number of applications and a never-ending stream of proposed improvements. The details of these latest modifications are not within the scope of this book and we concentrate on the essential, classical aspects. 

This book is the result of a cooperation between a chemometrician and a statistician. Usually, both sides have quite a different approach to describing statistical methods and applications—the former having a more practical approach and the latter being more formally oriented. The compromise as reflected in this book is hopefully useful for chemometricians, but it may also be useful for scientists and practitioners working in other disciplines—even for statisticians. The principles of multivariate statistical methods are valid, independent of the subject where the data come from. Of course, the focus here is on methods typically used in chemometrics, including techniques that can deal with a large number of variables. Since this book is an introduction, it was necessary to make a selection of the methods and applications that are used nowadays in chemometrics. 

Computation and practical use are further important concerns, and thus the R package chemometrics has been developed, including data sets used in this book as well as implementations of the methods described. Although some programming skills are required, the use of R has advantages because it is freeware and is continuously updated. Thus interested readers can go through the examples in this book and adapt the procedures to their own problems. Feedback is appreciated and it can lead to extension and improvement of the package. 

The book is at an introductory level, and only basic mathematical and statistical knowledge is assumed. However, we do not present chemometrics without equations —the book is intended for mathematically interested readers. Whenever possible, the formulae are in matrix notation, and for a clearer understanding many of them are visualized schematically. Appendix 2 might be helpful to refresh matrix algebra. 

For practical computation the software environment R is used. R is a powerful statistical software tool, it is freeware and can be downloaded at http //cran.r-project. org. Throughout the book we will present relevant R commands, and in Appendix 3 a brief introduction to R is given. An R-package chemometrics has been established it contains most of the data sets used in the examples and a number of newly written functions mentioned in this book. 

Chemometrics A Textbook published in 1988 by D. L. Massart et al. (1988) was for a long time the Bible (Blue Book) for chemometricians working in analytical chemistry. 

Recently, introductory books about chemometrics have been published by R. G. Brereton, Chemometrics—Data Analysis for the Laboratory and Chemical Plant (Brereton 2006) and Applied Chemometrics for Scientists (Brereton 2007), and by M. Otto, Chemometrics—Statistics and Computer Application in Analytical Chemistry (Otto 2007). Dedicated to quantitative chemical analysis, especially using infrared spectroscopy data, are A User-Friendly Guide to Multivariate Calibration and Classification (Naes et al. 2004), Chemometric Techniques for Quantitative Analysis (Kramer 1998), Chemometrics A Practical Guide (Beebe et al. 1998), and Statistics and Chemometrics for Analytical Chemistry (Miller and Miller 2000). 

In the earlier time of chemometrics until about 1990, a number of books have been published that may be rather of historical interest. Chemometrics—Applications of Mathematics and Statistics to Laboratory Systems (Brereton 1990), Chemical Applications of Pattern Recognition (Jurs and Isenhour 1975), Factor Analysis in... 

Univariate Verses Multivariate. The problem of working in univariate or multivariate environment was addressed in only one comment even though a whole book could be written on this topic alone. "Analysts should change their direction, wherever they can, to work in a multivariate area." Dr. Stalling said, "And the thing that impresses me so much about the chemometrics potential, is the capability of using multivariate statistics. How many problems can you define in the real world better in a univariate way Name me one "... 

With the foundations of chemometrics as a background, this section provides some detailed discussion of several specific chemometric tools. A truly complete discussion of all chemometric methods would require at least a book s worth of material. As a result, this discussion will focus on the tools that have been found... 

The contents of the book are intended to help a newcomer in the field, as well as to provide current information including developing technologies, for those who have practiced process analytical chemistry and PAT for some time. The main spectroscopic tools used for PAT are presented NIR, Raman, UV-Vis and FTIR, including not just the hardware, but many apphcation examples, and implementation issues. As chemometrics is central for use of many of these tools, a comprehensive chapter on this, now revised to more specifically address some issues relevant to PAT is included. In this second edition many of the previous chapters have been updated and revised, and additional chapters covering the important topic of sampling, and the additional techniques of NMR, fluorescence, and acoustic chemometrics are included. 

XZ/N VI RON MENTAL APPLICATIONS OF CHEMOMETRics are of interest because of the concern about the effects of chemicals on humans. The symposium upon which this book is based served as an important milestone in a process we, the editors, initiated in 1982. As members of the Environmental Protection Agency s Office of Toxic Substances (OTS), we have responsibilities for the acquisition and analysis of human and environmental exposure data in support of the Toxic Substances Control Act. OTS exposure studies invariably are complex and range from evaluating human body burden data (polychlorinated biphenyls in adipose tissue, for example) to documenting airborne asbestos levels in schools. 

Although the chemometric tools discussed in this book into both of these categories, most of the emphasis is on implicit modeling. Valid explicit models are notnmunon in practice and, therefore, implicit models are often necessary to anfee the data and/or construct predictive models. 

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