Comprehensive Chemometrics, Second Edition, Four Volume Set features expanded and updated coverage, along with new content that covers advances in the field since the previous edition published in 2009. Subject of note include updates in the fields of multidimensional and megavariate data analysis, omics data analysis, big chemical and biochemical data analysis, data fusion and sparse methods. The book follows a similar structure to the previous edition, using the same section titles to frame articles. Many chapters from the previous edition are updated, but there are also many new chapters on the latest developments.
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Table of Contents
Volume 1 (Statistics, Experimental Design, Optimization) a)�Statistics: This section covers the more fundamental aspects of statistics used in chemical and biochemical labs and industries, with chapters covering the sampling theory, quality and proficiency testing and control, and the introduction of statistical resampling, Bayesian methodologies and robust statistical methods.� Some of these last chapters of the section, for example Bayesian and robust approaches, should be updated and extended in the new edition of this section.
b)�Experimental Design This section gives a good coverage of the field of experimental design including methods for initial screening, quantitative study of the effects of the factors, response surface methodologies, mixture designs and non-classical strategies. These chapters help the experimenter in solving most of the situations encountered in practice. Either in this section or in others, there is the need of including more explicitly a discussion of multivariate extension of ANOVA for the study of the effects in well-designed experiments, an area of special importance nowadays, for instance in omics type of studies
c)�Optimization Many chemistry and chemometric data analysis problems can be formulated using numerical optimization strategies. Several chapters devoted to different optimization strategies are described in this section, including state of the art approaches for constrained and unconstrained optimization, sequential optimization, multi-criteria decision-making, and genetic algorithms. These methods could be complemented in the next edition of the work by including coverage of other methods such as particle swarm optimization and machine learning methods.
Volume 2 (Data preprocessing, Linear modeling, Unsupervised data mining) d)�Data preprocessing Data preprocessing is a fundamental step in the chemometrics workflow which is increasingly recognized. In the last few years, special efforts have been dedicated to systematize and classify the different approaches available, including signal pretreatment, background elimination, shift and alignment, scaling and normalization methods.� Especially important are preprocessing methods to handle size effects and their relation with compositional approaches. (These areas were insufficiently covered in the previous edition.)
e)�Linear Soft-Modeling Linear soft-modelling is at the core of many of the chemometric data analysis methods and this section gives full coverage of most of them, from bilinear-based methods such as principal component analysis (PCA), independent component analysis (ICA) and multivariate curve resolution (MCR), to trilinear (multilinear) based methods like PARAFAC- or Tucker3-based methods. Special topics to be extended in this section are simultaneous component analysis methods (along with their relation to extended ANOVA approaches in the experimental statistical design section) and very importantly maximum likelihood approaches which allow extending data analysis to more rigorous linear approaches where noise structure is included in the estimation of the parameters.
f)�Unsupervised Learning and Data Mining The most frequently used clustering methods used in unsupervised data analysis are described in this section. Classical methods (both linear and non-linear) and other data mapping techniques, density-based methods and tree-based methods are reviewed in detail with many examples of application in different fields. A relatively new field is Topological Data Analysis (TDA). The main goal of TDA is to study data shape, which may reveal important information about the studied systems or phenomena.� It can be useful in data exploration, clustering of numerous samples, and comparison of different platforms
Volume 3 (Linear and Non-linear Regression, Classification, Feature selection and Robust methods) g)�Linear Regression Modeling Multivariate linear regression modelling is another of the core elements of the chemometrics field and of its literature. Calibration strategies based on linear regression, such as PLS methods, including regression diagnostics, validation, variable selection methodology, handling missing data in regression, and adequate data preprocessing procedures are considered in detail in this section. In addition to these topics, more advanced robust regression approaches, transfer of calibration models, and three-way calibration regression methods are also presented.
h)�Non-linear regression Non-linear regression methods have been especially important for deterministic and hard modelling data fitting and parameter estimation type of problems.� These methods have been extended to multivariate data. Non-linear, soft modelling approaches are especially useful when no physical model is available and the data structure is highly non-linear. Especially important are the so-called kernel methods (support vector machines, SVM, kernel PCA or kernel PLS) that extend the potential of linear soft-modelling methods to non-linear data and problems. Also, locally weighted regression and other extension of linear methods are discussed in detail in this section. Neural networks in their different facets are also summarized.
i)�Classification Classification (or supervised pattern recognition) methods are another family of frequently used chemometric methods to solve many problems of class membership in analytical data from chemical, biological and environmental fields. Statistical discriminant analysis (linear, quadratic and higher dimensional), decision tree modeling and neural networks (revisited in detail here for classification purposes) are each covered in separate chapters. A last chapter shows some examples of how validation of the classifiers is performed.
j)�Feature Selection This section will be restructured and it will include new approaches proposed for feature selection in different fields. Four different chapters will describe different techniques used for feature selection by means of PLS (elimination of non-informative variables), sparse methods, genetic algorithms and wavelets (described in detail).
k)�Multivariate Robust Techniques A final section and chapter on robust techniques summarize the concepts, approaches and methods used when normal distribution assumptions either do not hold, describe the experimental data rather poorly, or are violated by the presence of outliers.� Robust alternatives to PLS regression and to linear discriminant analysis are described in detail and examples of their application, as well as uncertainty analysis of the obtained robust parameters using bootstrap permutation methods, are given.
Volume 4: Applications A whole volume is dedicated to the description of the application of chemometrics to relevant problems in a variety of fields, including environmental chemistry, food, health, sensory analysis, QSAR, spectroscopic imaging, microarray DNA, genomics, systems biology, chemoinformatics, process analytical technologies and process control, smart sensors and electrochemistry. This list of applications is by no means exhaustive, and the list of topics given here may require updating with the possible incorporation of new chapters in the next edition. Our aim is to document the development and use of chemometric methods in these fields and in new, emerging ones (including transcriptomics, DNA-sequencing metabolomics, proteomics, flow cytometry and others).
Authors
Steven Brown Department of Chemistry and Biochemistry, University of Delaware, USA. Expertise in chemical data analysis, including signal processing and detection, background removal, and transfer of multivariate calibrations. Data mining and knowledge discovery of chemical relationships involving multivariate data, including development of novel classifiers and hierarchical classification methods. Fusion of chemical sensor data for classification and calibration. Former EIC of Editor-in-Chief, Journal of Chemometrics (1994-2007), Editorial Board Member of Nature Scientific Reports and Journal of Chemometrics, Winner of Best Applied Chemometrics Paper 2015 (Wiley, Kowalski Award). Roma Tauler Institute of Chemical and Environmental Research, CSIC, Spain. Rom� Tauler graduated in Chemistry from the University of Barcelona (1977) and in 1984 obtained his Ph.D. degree in Analytical Chemistry at the University of Barcelona. He was Associate Professor at the University of Barcelona (Analytical Chemistry Dept) from 1987 to 2003. Since July 2003, he is Research Professor at the Institute of Environmental Assessment and Water Research (ID�A) of the Spanish National Research Council (CSIC). During these years, he carried out postdoc stays at Institut f�r Anorg. u. Anal. Chemie - Univ. of Innsbruck (Innsbruck, Austria, 1985 and 1989) and a sabbatical leave as a research scientist at the Center for Process Analytical Chemistry (CPAC) - University of Washington (Seattle, US, 1992). Until now, he has published more than 360 papers in ISI journals (WoS gives 11.656 citations and h-index 51 at Sept 7th, 2017). Nowadays, he is the Chief Editor of the Chemometrics and Intelligent Laboratory Systems (Elsevier) journal and Chief Editor of the Major Reference Work: Comprehensive Chemometrics, Chemical and Biochemical Data Analysis (Elsevier). He has been awarded an ERC Advanced Grant (2012) for the project "CHEMometric and High-Throughput Omics Analytical Methods for Assessment of Global Change Effects on Environmental and Biological Systems�. Other academic awards are the Award for Achievements in Chemometrics (Eastern Analytical Symposium, 2009) and the Kowalski Prize (Journal of Chemometrics, 2009).Main research interests are in Chemometrics, especially in the development of multivariate curve resolution methods for the analysis of multiway and multiset data, and for their applications to Omics Sciences, Environmental Chemistry, Analytical and Bioanalytical Chemistry (hyphenated chromatography, spectroscopy, imaging, mass spectrometry, sensor development), and Solution Chemistry (Equilibria and Kinetics).
Main research interests in Chemometrics, especially in the development of multivariate curve resolution methods for the analysis of multiway and muliset data, and for their applications to Omic Sciences, Environmental Chemistry, Analytical and Bioanalytical Chemistry (hyphenated chromatography, spectroscopy, imaging, mass spectrometry, sensor development), and Solution Chemistry (Equilibria and Kinetics). Winner of 2009 Award for Achievements in Chemometrics (Eastern Analytical Symposium) and 2009 Kowalski Prize from the Journal of Chemometrics. Former president of the Catalan Chemistry Society (2008-2013). He has published 354 papers in ISI journals and has an h-index of 53. Beata Walczak Department of Chemometrics, Institute of Chemistry, University of Silesia, Poland. Professor Walczak has been working in the Institute of Chemistry, University of Silesia since 1979, where now she is the head of the Department of Analytical Chemistry. She has authored and co-authored around 165 scientific papers, 400 conference papers, and delivered many invited lectures at international chemistry meetings. Currently she acts as Editor of the journal Chemometrics and Intelligent Laboratory Systems and of 'Data Handling in Chemistry and Technology' (the Elsevier book series), and as a member of the editorial boards of Talanta, Analytical Letters, J. Chemometrics, and Acta Chromatographica. She has been involved in chemometrics since the early 1990s and her main scientific interests are in all aspects of data exploration and modelling (dealing with missing and censored data, dealing with outliers, data representatively, enhancement of instrumental signals, signal warping, data compression, linear and non-linear projections, development of modeling approaches, feature selection techniques etc.). She has an h-index of 40.
