Comprehensive Chemometrics: Chemical and Biochemical Data Analysis

Description

Comprehensive Chemometrics, Second Edition 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. Volume 1 Editors in Chief Section Editors In Memory of Roger Phan Tan Luu • Michele Forina Contributors to Volume 1 Subject Classification Preface Contents of All Volumes 1.01 Quality of Analytical Measurements: Statistical Methods for Internal Validation • M Cruz Ortiz, Luis A Sarabia, M Sagrario Sánchez, and Ana Herrero 1.02 Proficiency Testing in Analytical Chemistry • M Thompson and SLR Ellison 1.03 Quality of Analytical Measurements: Univariate Regression • MC Ortiz, MS Sánchez, and LA Sarabia 1.04 Robust Multivariate Statistical Methods • Mia Hubert 1.05 Bayesian Methodology in Statistics • JM Bernardo 1.06 Robust Methods for High-Dimensional Data • Mia Hubert 1.07 An Introduction to the Theory of Sampling: An Essential Part of Total Quality Management • FF Pitard 1.08 Representative Sampling, Data Quality, Validationd—A Necessary Trinity in Chemometrics • KH Esbensen and LP Julius 1.09 Introduction Experimental Designs • R Cela and R Phan-Tan-Luu 1.10 Screening Strategies • Rafael Cela Torrijos and Roger Phan-Tan-Luu 1.11 The Study of Experimental Factors • Rolf Carlson and Johan E Carlson 1.12 Response Surface Methodology • Luis A Sarabia, M Cruz Ortiz, and M Sagrario Sánchez 1.13 Experimental Design for Mixture Studies • D Voinovich, B Campisi, R Phan-Tan-Luu, and A Beal 1.14 Nonclassical Experimental Designs • Aurélie Beal and Roger Phan-Tan-Luu 1.15 Designing a Multi-Component Calibration Experiment: Basic Principles and Diagonal Approach • Andrey Bogomolov 1.16 The Essentials on Linear Regression, ANOVA, General Linear and Linear Mixed Models for the Chemist • Bernadette Govaerts, Bernard Francq, Rebecca Marion, Manon Martin, and Michel Thiel 1.17 Multiset Data Analysis: ANOVA Simultaneous Component Analysis and Related Methods • HCJ Hoefsloot, DJ Vis, JA Westerhuis, AK Smilde, and JJ Jansen 1.18 Regularized Multivariate Analysis of Variance • Jasper Engel, Kas J Houthuijs, Vasilis Vasiliou, and Georgia Charkoftaki 1.19 ANOVA-Target Projection (ANOVA-TP) • Federico Marini and Beata Walczak 1.20 Constrained and Unconstrained Optimization • BW Bader 1.21 Sequential Optimization Methods • Bieke Dejaegher and Yvan Vander Heyden 1.22 Optimisation: Steepest Ascent, Steepest Descent and Gradient Methods • Richard G Brereton 1.23 Multicriteria Decision-Making Methods • M Pavan and R Todeschini 1.24 Genetic Algorithms in Chemistry • Riccardo Leardi 1.25 A Guided Tour of Penalties • Paul HC Eilers 1.26 Particle Swarm Optimization • Federico Marini and Beata Walczak Volume 2 Editors in Chief Section Editors In Memory of Roger Phan Tan Luu • Michele Forina Contributors to Volume 2 Subject Classification Preface Contents of All Volumes 2.01 Introduction to Linear Soft-Modeling • Anna de Juan and Romà Tauler 2.02 Principal Component Analysis: Concept, Geometrical Interpretation, Mathematical Background, Algorithms, History, Practice • KH Esbensen and P Geladi 2.03 Principal Component Analysis • Paul Geladi and Johan Linderholm 2.04 Independent Component Analysis • F Westad and M Kermit 2.05 Independent Component Analysis in Analytical Chemistry • Hadi Parastar 2.06 Introduction to Multivariate Curve Resolution • Sarah C Rutan, Anna de Juan, and Romà Tauler 2.07 Two-Way Data Analysis: Evolving Factor Analysis • M Maeder and A de Juan 2.08 Two-Way Data Analysis: Detection of Purest Variables • Willem Windig, Andrey Bogomolov, and Sergey Kucheryavskiy 2.09 Two-Way Data Analysis: Multivariate Curve Resolution: Noniterative Resolution Methods • Zhimin Zhang, Pan Ma, and Hongmei Lu 2.10 Two-Way Data Analysis: Multivariate Curve Resolution, Iterative Methods • Anna de Juan, Sarah C Rutan, and Romà Tauler 2.11 Multivariate Curve Resolutiond—Error in Curve Resolution • Romà Tauler and Marcel Maeder 2.12 On the Ambiguity Underlying Multivariate Curve Resolution Methods • Mathias Sawall, Henning Schröder, Denise Meinhardt, and Klaus Neymeyr 2.13 Multiway Data Analysis: Eigenvector-Based Methods • J Ferré, R Boqué, and NM Faber 2.14 Multilinear Models, Iterative Methods • Giorgio Tomasi, Evrim Acar, and Rasmus Bro 2.15 Multiset Data Analysis: Extended Multivariate Curve Resolution • Romà Tauler, Marcel Maeder, and Anna de Juan 2.16 Tensor Similarity in Chemometrics • Frederik Van Eeghem and Lieven De Lathauwer 2.17 Bayesian Methods for Factor Analysis in Chemometrics • Eun Sug Park and Romà Tauler 2.18 Time Series Analysis Methods in Chemometrics • Steven D Brown 2.19 Other Topics in Soft-Modeling: Maximum Likelihood-Based Soft-Modeling Methods • PD Wentzell 2.20 Figures of Merit • Franco Allegrini and Alejandro C Olivieri 2.21 Unsupervised Data Mining: Introduction • D Coomans, C Smyth, I Lee, T Hancock, and J Yang 2.22 Data Mapping: Linear Methods versus Nonlinear Techniques • R Wehrens 2.23 Tree-Based Clustering and Extensions • T Hancock and C Smyth 2.24 Model-Based Clustering • GJ McLachlan, SI Rathnayake, and SX Lee 2.25 Common Clustering Algorithms • Ickjai Lee and Jianhua Yang 2.26 Density-Based Clustering Methods • M Daszykowski and B Walczak 2.27 Feature Selection: Introduction • BK Lavine 2.28 Feature Selection in the Wavelet Domain: Adaptive Wavelets • DA Donald, YL Everingham, LW McKinna, and D Coomans 2.29 Missing Data • F Arteaga, A Folch-Fortuny, and A Ferrer 2.30 Compositional Data Analysis in Chemometrics • Peter Filzmoser and Karel Hron 2.31 Sparse Methods • Ahmad Mani-Varnosfaderani Volume 3 Editors in Chief Section Editors In Memory of Roger Phan Tan Luu • Michele Forina Contributors to Volume 3 Subject Classification Preface Contents of All Volumes 3.01 Pre-processing Methods • Jean-Michel Roger, Jean-Claude Boulet, Magida Zeaiter, and Douglas N Rutledge 3.02 Evaluation of Preprocessing Methods • H Jonsson and J Gabrielsson 3.03 Model-Based Pre-Processing in Vibrational Spectroscopy • Achim Kohler, Johanne Heitmann Solheim, Valeria Tafintseva, Boris Zimmermann, and Volha Shapaval 3.04 Normalization and Closure • M Bylesjö, O Cloarec, and M Rantalainen 3.05 Variable Shift and Alignment • Renger H Jellema, Abel Folch-Fortuny, and Margriet MWB Hendriks 3.06 Background Estimation, Denoising, and Preprocessing • J Trygg, J Gabrielsson, and T Lundstedt 3.07 Denoising and Signal-to-Noise Ratio Enhancement: Classical Filtering • DF Thekkudan and SC Rutan 3.08 Denoising and Signal-to-Noise Ratio Enhancement: Derivatives • V-M Taavitsainen 3.09 Denoising and Signal-to-Noise Ratio Enhancement: Splines • V-M Taavitsainen 3.10 Data Quality and Denoising: A Review • MS Reis, PM Saraiva, and BR Bakshi 3.11 Model Based Preprocessing and Background Elimination: OSC, OPLS, and O2PLS • M Bylesjö and M Rantalainen 3.12 Calibration Methodologies • John H Kalivas and Steven D Brown 3.13 Linear Regression Modeling: Variable Selection • Roberto Kawakami Harrop Galvão, Mário César Ugulino de Araújo, and Sófacles Figueredo Carreiro Soares 3.14 An Elemental Perspective on Partial Least Squares • William S Rayens 3.15 Multivariate Approaches: UVE-PLS • V Centner 3.16 Data and Model Fusion in Chemometrics • Steven D Brown 3.17 Multi-Block and Three-Way Data Analysis • Mohamed Hanafi, El Mostafa Qannari, and Benoit Jaillais 3.18 Transfer of Multivariate Calibration Models • Steven D Brown 3.19 Robust Multivariate Methods in Chemometrics • Peter Filzmoser, Sven Serneels, Ricardo Maronna, and Christophe Croux 3.20 Regression Diagnostics • Joan Ferré Baldrich 3.21 Model-Based Data Fitting • M Maeder, N McCann, S Clifford, and G Puxty 3.22 Linear Approaches for Nonlinear Modeling • H Chen and BR Bakshi 3.23 Computationally Intensive Nonlinear Regression Methods • Bin Li, Bhavik R Bakshi, and Prem Goel 3.24 Non-linear Modeling: Neural Networks • Federico Marini 3.25 Feed-Forward Neural Networks • BK Lavine and TR Blank 3.26 Kernel Methods • J Suykens 3.27 Classification: Basic Concepts • BK Lavine and WS Rayens 3.28 Validation of Classifiers • BK Lavine 3.29 Statistical Discriminant Analysis • BK Lavine and WS Rayens 3.30 Soft Independent Modeling by Class Analogy • Alexey L Pomerantsev and Oxana Ye Rodionova 3.31 Decision Tree Modeling • Steven D Brown and Anthony J Myles 3.32 Random Forest and Ensemble Methods • George Stavropoulos, Robert van Voorstenbosch, Frederik-Jan van Schooten, and Agnieszka Smolinska 3.33 Genetic Algorithms for Variable Selection and Pattern Recognition • Barry K Lavine, Collin G White, and Charles E Davidson 3.34 Multi Way Classification • Marina Cocchi, Mario Li Vigni, and Caterina Durante 3.35 Deep Learning Theoretical Chapter for Chemometrician • Robert van Vorstenbosch, Agnieszka Smolinska, and Lionel Blanchet Volume 4 Editors in Chief Section Editors In Memory of Roger Phan Tan Luu • Michele Forina Contributors to Volume 4 Subject Classification Preface Contents of All Volumes 4.01 Chemometrics in Electrochemistry • M Esteban, C Ariño, and JM Díaz-Cruz 4.02 Chemometrics in the Pharmaceutical Industry • Benoît Igne, Christian Airiau, Sameer Talwar, and Elyse Towns 4.03 Environmental Chemometrics • Philip K Hopke 4.04 Resampling and Testing in Regression Models with Environmetrical Applications • J Roca-Pardiñas, C Cadarso-Suárez, and W González-Manteiga 4.05 Application of Chemometrics in the Food Sciences • Paolo Oliveri, Cristina Malegori, Eleonora Mustorgi, and Monica Casale 4.06 Chemometrics in Forensics • Marcelo M Sena, Werickson FC Rocha, Jez WB Braga, Carolina S Silva, and Aaron Urbas 4.07 Chemometric Analysis of Sensory Data • D Brynn Hibbert 4.08 Smart Sensors • Jordi Fonollosa 4.09 Statistical Control of Measures and Processes • AJ Ferrer-Riquelme 4.10 Best Practice and Performance of Hardware in Process Analytical Technology (PAT) • Rudolf W Kessler and Waltraud Kessler 4.11 Multivariate Statistical Process Control and Process Control, Using Latent Variables • T Kourti 4.12 Batch Process Modeling and MSPC • S Wold, N Kettaneh-Wold, JF MacGregor, and KG Dunn 4.13 Comprehensive Chemometrics • Shuxia Guo, Oleg Ryabchykov, Nairveen Ali, Rola Houhou, and Thomas Bocklitz 4.14 Chemometrics in NIR Hyperspectral Imaging: Theory and Applications in the Agricultural Crops and Products Sector • Juan Antonio Fernández Pierna, Philippe Vermeulen, Damien Eylenbosch, James Burger, Bernard Bodson, Pierre Dardenne, and Vincent Baeten 4.15 Mass Spectrometry Imaging: Chemometric Data Analysis • Joaquim Jaumot and Carmen Bedia 4.16 Fast Analysis, Processing and Modeling of Hyperspectral Videos: Challenges and Possible Solutions • Raffaele Vitale, Petter Stefansson, Federico Marini, Cyril Ruckebusch, Ingunn Burud, and Harald Martens 4.17 Image Processing in Chemometrics • Siewert Hugelier, Raffaele Vitale, and Cyril Ruckebusch 4.18 Chemometrics Analysis of Big Data • José Camacho and Edoardo Saccenti 4.19 Systems Biology • L Coulier, S Wopereis, C Rubingh, H Hendriks, M Radonjic, and RH Jellema 4.20 Analysis of Metabolomics DatadA Chemometrics Perspective • Julien Boccard and Serge Rudaz 4.21 Data Processing for RNA/DNA Sequencing • Inmaculada Fuertes, Maria Vila-Costa, Jana Asselman, Benjamín Piña, and Carlos Barata 4.22 Analysis of Megavariate Data in Functional Omics • EF Mosleth, A McLeod, I Rud, L Axelsson, LE Solberg, B Moen, KME Gilman, EM Færgestad, A Lysenko, C Rawlings, SN Dankel, G Mellgren, F Barajas-Olmos, LS Orozco, S Sæbø, L Gidskehaug, A Oust, A Kohler, H Martens, and KH Liland 4.23 Spectral Map Analysis of Microarray Data • L Bijnens, R Verbeeck, HW Göhlmann, W Talloen, RA Ion, PJ Lewi, and L Wouters 4.24 Chemometrics in Flow Cytometry • Gerjen H Tinnevelt and Jeroen J Jansen 4.25 Chemometrics for QSAR Modeling • Roberto Todeschini, Viviana Consonni, Davide Ballabio, and Francesca Grisoni 4.26 Chemoinformatics • J Polanski 4.27 High-Performance GRID Computing in Chemoinformatics • N Sim, D Konovalov, and D Coomans Index Author Index