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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
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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.”
Comprehensive Chemometrics: Chemical and Biochemical Data Analysis
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Comprehensive Chemometrics: Chemical and Biochemical Data Analysis
Charts, tables, and illustrations make extensive use of color throughout. The online version of Comprehensive Chemometrics features additional content for download.
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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
Comprehensive Chemometrics: Chemical and Biochemical Data Analysis
*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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Comprehensive Chemometrics: Chemical and Biochemical Data Analysis
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