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AVAILABLE IN PRINT AND ONLINE MARCH 2009

Comprehensive ChemometricsChemical and Biochemical Data Analysis Four-Volume SetEditors-in-Chief: Steven D. Brown, University of Delaware, Newark, USARomà Tauler, Institute of Environmental Assessment and Water Research, CSIC, Barcelona, SpainBeata Walczak, University of Silesia, Katowice, Poland

Available in print and online March 2009

INTRODUCTORY PRINT PRICE*$1,595 / €1,090 / £865

ISBN: 9780444527028 / 4-Volume Set / Hardback / 2,896 pagesList Price: $1,995 / €1,360 / £1,080For online pricing, visit www.info.sciencedirect.com* Introductory price expires end of third month after publication. All prices are subject to change.

Does your work involve the analysis of chemical and biochemical data?

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Editors-in-Chief:

Steven D. Brown Dr. Brown (PhD, University of Washington) has taught at the University of California, Berkeley; Washington State University; and University of Delaware, USA, where he is presently Willis F. Harrington Professor. He has served as a section president of the American Chemical Society, and as President of the North American Chapter of the International Chemometrics Society. One of the three founding editors of Journal of Chemometrics, he served for 20 years, first as its North American editor, and then for 12 years as its editor-in-chief. In 1986 he was the first recipient of the EAS Award in Chemometrics.

Romà Tauler Romà Tauler is Professor of the Department of Environmental Chemistry at the Institute of Environmental Assessment and Water Research (IDÆA), Spanish Council of Scientific Research (CSIC) in Barcelona, Spain. At present, he is the editor-in-chief of the journal Chemometrics and Intelligent Laboratory Systems. Dr. Tauler has published more than 200 research papers, most of them in the field of chemometrics and its applications, and in particular in the area of new multivariate resolution methods. In recent years he has focused more on the investigation of environmental problems.

Beata Walczak Currently Dr. Walczak is head of the Department of Chemometrics, Institute of Chemistry, University of Silesia, Katowice, Poland. Her primary interests lie in all aspects of data exploration and modeling, including missing and censored data, outliers, data representativity, enhancement of instrumental signals, signal warping, data compression, and linear and non-linear projections. She acts as editor of Chemometrics and Intelligent Laboratory Systems and Data Handling in Science and Technology (the Elsevier book series), and is a member of the editorial boards of Talanta, Analytical Letters, and Acta Chromatographica.

Comprehensive Chemometrics consolidates coverage of the field and allows new and experienced practitioners to analyze chemical and biochemical data in their research. This new reference work meets the needs of scientists, statisticians, and academics working in a range of disciplines, from chemistry and engineering to life sciences.

AIMS AND SCOPEPresents and explains each technique—including its merits »and limitations—through introductions, detailed reviews, and extensive full-color illustrations

Provides a global perspective on this rapidly evolving field, with »contributions from authors around the world, working in both academia and industry

Offers two content formats—print and online—the latter of »which provides anytime, anywhere access for multiple users and superior search functionality via ScienceDirect

INTRODUCTORY PRINT PRICE*$1,595 / €1,090 / £865

ISBN: 97804445270284-Volume Set Hardback / 2,896 pagesList Price: $1,995 / €1,360 / £1,080For online pricing, visit www.info.sciencedirect.com* Introductory price expires end of third month after publication. All prices are subject to change.

Comprehensive Chemometrics: Chemical and Biochemical Data Analysis


SECTION EDITORSLutgarde Buydens, Radboud University of Nijmegen, Netherlands—Non-linear Regression

Danny Coomans, James Cook University, Townsville, Queensland, Australia—Data Mining

Anna de Juan, University of Barcelona, Spain—Soft Modeling

John H. Kalivas, Idaho State University, Pocatello, USA—Linear Regression

Barry K. Lavine, Oklahoma State University, Stillwater, USA—Classification and Feature Selection

Riccardo Leardi, University of Genova, Italy—Optimization Methods

Roger Phan-Tan-Luu, Université Paul Cézanne, Marseille, France—Experimental Design

Luis A. Sarabia, University of Burgos, Spain—Statistical Preliminaries

Johan Trygg, Umeå University, Sweden—Data Preprocessing

Pierre Van Espen, University of Antwerp, Belgium—Robust Approaches

Pictured from top: Editors Steven D. Brown, Romà Tauler, and Beata Walczak

A Message from the Editors-in-Chief“The ready availability of chemometric software—coupled with the increasing need for rigorous, systematic examination of ever-larger and more sophisticated sets of measurements from instrumentation—has generated strong interest in reliable methods for converting the mountains of measurements into more manageable piles of results, and for converting those results into nuggets of useful information. Interest in applications of chemometrics has spread well beyond chemists with a need to understand and interpret their measurements; now chemometrics is helping to make important contributions in process engineering, systems biology, environmental science, and other disciplines that rely on chemical instrumentation.

“The four volumes in this work include ca. 90 chapters, making this the most wide-reaching and detailed overview of the field of chemometrics ever published. Comprehensive Chemometrics offers depth and rigor to the new practitioner entering the field, and breadth and varied perspectives on current literature to more experienced practitioners aiming to expand their horizons. Software and datasets, both of which are especially valuable to those learning the methods, are integrated throughout the chapters. The coverage is not only comprehensive, it is authoritative as well; authors contributing to Comprehensive Chemometrics are among the most distinguished practitioners of the field.”




Charts, tables, and illustrations make extensive use of color throughout. The online version of Comprehensive Chemometrics features additional content for download.


VOLUME 3Linear Regression Modeling Calibration Methodologies

Regression Diagnostics

Validation and Error

Preprocessing Methods

Variable Selection

Missing Data

Robust Calibration

Transfer of Multivariate Calibration Models

Three-Way Calibration

Non-Linear Regression Model-Based Data Fitting

Kernel Methods

Linear Approaches for Non-Linear Modeling

Other Methods in Non-Linear Regression

Neural Networks

ClassificationClassification: Basic Concepts

Statistical Discriminant Analysis

Decision Tree Modeling in Classification

Feed-Forward Neural Networks

Validation of Classifiers

Feature SelectionFeature Selection: Introduction

Multivariate Approaches: UVE-PLS

Multivariate Approaches to Classification Using Genetic Algorithms

Feature Selection in the Wavelet Domain: Adaptive Wavelets

Multivariate Robust Techniques Robust Multivariate Methods in Chemometrics

VOLUME 4Applications Representative Sampling, Data Quality, Validation—A Necessary Trinity in Chemometrics

Multivariate Statistical Process Control and Process Control, Using Latent Variables

Environmental Chemometrics

Application of Chemometrics to Food Chemistry

Chemometrics in QSAR

Spectroscopic Imaging

Spectral Map Analysis of Microarray Data

Analysis of Megavariate Data in Functional Genomics

Systems Biology

Chemometrics’ Role within the PAT Context: Examples from Primary Pharmaceutical Manufacturing

Smart Sensors

Chemometric Analysis of Sensory Data

Chemometrics in Electrochemistry

Chemoinformatics

On High-Performance GRID Computing in Chemoinformatics


*Abridged contents are provisional and subject to change.

CONTENTS*VOLUME 1Statistics An Introduction to the Theory of Sampling: An Essential Part of Total Quality Management

Quality of Analytical Measurements: Statistical Methods for Internal Validation

Proficiency Testing in Analytical Chemistry

Statistical Control of Measures and Processes

Quality of Analytical Measurements: Univariate Regression

Resampling and Testing in Regression Models with Environmetrical Applications

Robust and Nonparametric Statistical Methods

Bayesian Methodology in Statistics

Experimental Design Experimental Design: Introduction

Screening Strategies

The Study of Experimental Factors

Response Surface Methodology

Experimental Design for Mixture Studies

Nonclassical Experimental Designs

Experimental Designs: Conclusions, Terminology, and Symbols

Optimization Constrained and Unconstrained Optimization

Sequential Optimization Methods

Steepest Ascent, Steepest Descent, and Gradient Methods

Multicriteria Decision-Making Methods

Genetic Algorithms

VOLUME 2Data Preprocessing Background Estimation, Denoising, and Preprocessing

Denoising and Signal-to-Noise Ratio Enhancement: Classical FilteringWavelet Transform and Fourier TransformDerivatives Splines

Variable Shift and Alignment

Normalization and Closure

Model-Based Preprocessing and Background Elimination: OSC, OPLS, and O2PLS

Standard Normal Variate, Multiplicative Signal Correction, and Extended Multiplicative Signal Correction Preprocessing in Biospectroscopy

Batch Process Modeling and MSPC

Evaluation of Preprocessing Methods

Linear Soft-Modeling Linear Soft-Modeling: Introduction

Principal Component Analysis: Concept, Geometrical Interpretation, Mathematical Background, Algorithms, History, Practice

Independent Component Analysis

Introduction to Multivariate Curve Resolution

Two-Way Data Analysis:Evolving Factor Analysis Detection of Purest VariablesMultivariate Curve Resolution:

Noniterative Resolution MethodsIterative Resolution Methods Error in Curve Resolution

Multiway Data Analysis: Eigenvector-Based Methods

Multilinear Models: Iterative Methods

Multiset Data Analysis: ANOVA Simultaneous Component Analysis and Related MethodsExtended Multivariate Curve Resolution

Other Topics in Soft-Modeling: Maximum Likelihood-Based Soft-Modeling Methods

Unsupervised Data MiningUnsupervised Data Mining: Introduction

Common Clustering Algorithms

Data Mapping: Linear Methods versus Non-Linear Techniques

Density-Based Clustering Methods

Model-Based Clustering

Tree-Based Clustering and Extensions



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