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Chemometrics: Data Driven Extraction for Science 2nd edition [Paperback / softback]

(University of Bristol, UK)
  • Format: Paperback / softback, 464 pages, height x width x depth: 277x216x28 mm, weight: 1293 g
  • Pub. Date: 30-Mar-2018
  • Publisher: John Wiley & Sons Inc
  • ISBN-10: 1118904664
  • ISBN-13: 9781118904664
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Chemometrics: Data Driven Extraction for Science 2nd editionLarger Image
  • Format: Paperback / softback, 464 pages, height x width x depth: 277x216x28 mm, weight: 1293 g
  • Pub. Date: 30-Mar-2018
  • Publisher: John Wiley & Sons Inc
  • ISBN-10: 1118904664
  • ISBN-13: 9781118904664
Other books in subject:
Permanent link: https://www.kriso.ee/db/9781118904664.html
Keywords:

A new, full-color, completely updated edition of the key practical guide to chemometrics

This new edition of this practical guide on chemometrics, emphasizes the principles and applications behind the main ideas in the field using numerical and graphical examples, which can then be applied to a wide variety of problems in chemistry, biology, chemical engineering, and allied disciplines. Presented in full color, it features expansion of the principal component analysis, classification, multivariate evolutionary signal and statistical distributions sections, and new case studies in metabolomics, as well as extensive updates throughout. Aimed at the large number of users of chemometrics, it includes extensive worked problems and chapters explaining how to analyze datasets, in addition to updated descriptions of how to apply Excel and Matlab for chemometrics. 

Chemometrics: Data Driven Extraction for Science, Second Edition offers chapters covering: experimental design, signal processing, pattern recognition, calibration, and evolutionary data. The pattern recognition chapter from the first edition is divided into two separate ones: Principal Component Analysis/Cluster Analysis, and Classification. It also includes new descriptions of Alternating Least Squares (ALS) and Iterative Target Transformation Factor Analysis (ITTFA). Updated descriptions of wavelets and Bayesian methods are included.

  • Includes updated chapters of the classic chemometric methods (e.g. experimental design, signal processing, etc.)
  • Introduces metabolomics-type examples alongside those from analytical chemistry
  • Features problems at the end of each chapter to illustrate the broad applicability of the methods in different fields
  • Supplemented with data sets and solutions to the problems on a dedicated website

Chemometrics: Data Driven Extraction for Science, Second Edition is recommended for post-graduate students of chemometrics as well as applied scientists (e.g. chemists, biochemists, engineers, statisticians) working in all areas of data analysis.

Reviews

"... fills a gap in the chemometrics literature landscape. With its unique approach of learning-by-doing it is best suited for practitioners, which do not want to dig too deep into the theory and are not interested in a full coverage of methods. Nevertheless, the most important and usual applied chemometrics methods are introduced... The example data sets of the book are also worth exploring by itself, because they are well chosen and nicely structured." Thomas Bocklitz, Analytical and Bioanalytical Chemistry (2019)

Preface to Second Edition xi
Preface to First Edition xiii
Acknowledgements xv
About the Companion Website xvii
1 Introduction 1(10)
1.1 Historical Parentage
1(2)
1.1.1 Applied Statistics
1(1)
1.1.2 Statistics in Analytical and Physical Chemistry
2(1)
1.1.3 Scientific Computing
3(1)
1.2 Developments since the 1970s
3(1)
1.3 Software and Calculations
4(2)
1.4 Further Reading
6(2)
1.4.1 General
6(1)
1.4.2 Specific Areas
7(1)
References
8(3)
2 Experimental Design 11(90)
2.1 Introduction
11(3)
2.2 Basic Principles
14(29)
2.2.1 Degrees of Freedom
14(3)
2.2.2 Analysis of Variance
17(6)
2.2.3 Design Matrices and Modelling
23(6)
2.2.4 Assessment of Significance
29(9)
2.2.5 Leverage and Confidence in Models
38(5)
2.3 Factorial Designs
43(19)
2.3.1 Full Factorial Designs
44(5)
2.3.2 Fractional Factorial Designs
49(6)
2.3.3 Plackett-Burman and Taguchi Designs
55(2)
2.3.4 Partial Factorials at Several Levels: Calibration Designs
57(5)
2.4 Central Composite or Response Surface Designs
62(8)
2.4.1 Setting up the Design
62(3)
2.4.2 Degrees of Freedom
65(1)
2.4.3 Axial Points
66(1)
2.4.4 Modelling
67(2)
2.4.5 Statistical Factors
69(1)
2.5 Mixture Designs
70(12)
2.5.1 Mixture Space
70(1)
2.5.2 Simplex Centroid
71(3)
2.5.3 Simplex Lattice
74(2)
2.5.4 Constraints
76(5)
2.5.5 Process Variables
81(1)
2.6 Simplex Optimisation
82(4)
2.6.1 Fixed Sized Simplex
82(2)
2.6.2 Elaborations
84(1)
2.6.3 Modified Simplex
84(2)
2.6.4 Limitations
86(1)
Problems
86(15)
3 Signal Processing 101(62)
3.1 Introduction
101(2)
3.1.1 Environmental and Geological Processes
101(1)
3.1.2 Industrial Process Control
101(1)
3.1.3 Chromatograms and Spectra
102(1)
3.1.4 Fourier Transforms
102(1)
3.1.5 Advanced Methods
102(1)
3.2 Basics
103(9)
3.2.1 Peak shapes
103(4)
3.2.2 Digitisation
107(2)
3.2.3 Noise
109(3)
3.2.4 Cyclicity
112(1)
3.3 Linear Filters
112(10)
3.3.1 Smoothing Functions
112(4)
3.3.2 Derivatives
116(2)
3.3.3 Convolution
118(4)
3.4 Correlograms and Time Series Analysis
122(6)
3.4.1 Auto-correlograms
122(2)
3.4.2 Cross-correlograms
124(3)
3.4.3 Multivariate Correlograms
127(1)
3.5 Fourier Transform Techniques
128(14)
3.5.1 Fourier Transforms
128(7)
3.5.2 Fourier Filters
135(5)
3.5.3 Convolution Theorem
140(2)
3.6 Additional Methods
142(11)
3.6.1 Kalman Filters
142(3)
3.6.2 Wavelet Transforms
145(3)
3.6.3 Bayes' Theorem
148(2)
3.6.4 Maximum Entropy
150(3)
Problems
153(10)
4 Principal Component Analysis and Unsupervised Pattern Recognition 163(52)
4.1 Introduction
163(1)
4.1.1 Exploratory Data Analysis
163(1)
4.1.2 Cluster Analysis
164(1)
4.2 The Concept and Need for Principal Components Analysis
164(7)
4.2.1 History
164(1)
4.2.2 Multivariate Data Matrices
165(1)
4.2.3 Case Studies
166(5)
4.2.4 Aims of PCA
171(1)
4.3 Principal Components Analysis: The Method
171(12)
4.3.1 Scores and Loadings
171(4)
4.3.2 Rank and Eigenvalues
175(8)
4.4 Factor Analysis
183(1)
4.5 Graphical Representation of Scores and Loadings
184(7)
4.5.1 Scores Plots
185(3)
4.5.2 Loadings Plots
188(3)
4.6 Pre-processing
191(8)
4.6.1 Transforming Individual Elements of a Matrix
191(2)
4.6.2 Row Scaling
193(1)
4.6.3 Mean Centring
194(3)
4.6.4 Standardisation
197(2)
4.6.5 Further Methods
199(1)
4.7 Comparing Multivariate Patterns
199(2)
4.7.1 Biplots
200(1)
4.7.2 Procrustes Analysis
201(1)
4.8 Unsupervised Pattern Recognition: Cluster Analysis
201(6)
4.8.1 Similarity
202(2)
4.8.2 Linkage
204(2)
4.8.3 Next Steps
206(1)
4.8.4 Dendrograms
206(1)
4.9 Multi-way Pattern Recognition
207(3)
4.9.1 Tucker3 Models
207(1)
4.9.2 Parallel Factor Analysis (PARAFAC)
208(1)
4.9.3 Unfolding
209(1)
Problems
210(5)
5 Classification and Supervised Pattern Recognition 215(50)
5.1 Introduction
215(1)
5.1.1 Background
215(1)
5.1.2 Case Study
216(1)
5.2 Two-Class Classifiers
216(13)
5.2.1 Distance-Based Methods
217(7)
5.2.2 Partial Least-Squares Discriminant Analysis
224(2)
5.2.3 K Nearest Neighbours
226(3)
5.3 One-Class Classifiers
229(7)
5.3.1 Quadratic Discriminant Analysis
229(3)
5.3.2 Disjoint PCA and SIMCA
232(4)
5.4 Multi-Class Classifiers
236(1)
5.5 Optimisation and Validation
237(9)
5.5.1 Validation
238(7)
5.5.2 Optimisation
245(1)
5.6 Significant Variables
246(6)
5.6.1 Partial Least-Squares Discriminant Loadings and Weights
248(2)
5.6.2 Univariate Statistical Indicators
250(1)
5.6.3 Variable Selection for SIMCA
251(1)
Problems
252(13)
6 Calibration 265(58)
6.1 Introduction
265(2)
6.1.1 History, Usage and Terminology
265(2)
6.1.2 Case Study
267(1)
6.2 Univariate Calibration
267(9)
6.2.1 Classical Calibration
269(3)
6.2.2 Inverse Calibration
272(2)
6.2.3 Intercept and Centring
274(2)
6.3 Multiple Linear Regression
276(8)
6.3.1 Multi-detector Advantage
276(1)
6.3.2 Multi-wavelength Equations
277(3)
6.3.3 Multivariate Approaches
280(4)
6.4 Principal Components Regression
284(5)
6.4.1 Regression
284(3)
6.4.2 Quality of Prediction
287(2)
6.5 Partial Least Squares Regression
289(13)
6.5.1 PLS1
289(5)
6.5.2 PLS2
294(3)
6.5.3 Multi-way PLS
297(5)
6.6 Model Validation and Optimisation
302(7)
6.6.1 Auto-prediction
302(1)
6.6.2 Cross-validation
303(2)
6.6.3 Independent Test Sets
305(4)
Problems
309(14)
7 Evolutionary Multivariate Signals 323(52)
7.1 Introduction
323(2)
7.2 Exploratory Data Analysis and Pre-processing
325(16)
7.2.1 Baseline Correction
325(1)
7.2.2 Principal Component-Based Plots
325(4)
7.2.3 Scaling the Data after PCA
329(3)
7.2.4 Scaling the Data before PCA
332(7)
7.2.5 Variable Selection
339(2)
7.3 Determining Composition
341(14)
7.3.1 Composition
341(1)
7.3.2 Univariate Methods
342(3)
7.3.3 Correlation- and Similarity-Based Methods
345(3)
7.3.4 Eigenvalue-Based Methods
348(4)
7.3.5 Derivatives
352(3)
7.4 Resolution
355(10)
7.4.1 Selectivity for All Components
356(4)
7.4.2 Partial Selectivity
360(2)
7.4.3 Incorporating Constraints: ITTFA, ALS and MCR
362(3)
Problems
365(10)
A Appendix 375(54)
A.1 Vectors and Matrices
375(2)
A.1.1 Notation and Definitions
375(1)
A.1.2 Matrix and Vector Operations
375(2)
A.2 Algorithms
377(4)
A.2.1 Principal Components Analysis
377(1)
A.2.2 PLS1
378(1)
A.2.3 PLS2
379(1)
A.2.4 Tri-Linear PLS1
380(1)
A.3 Basic Statistical Concepts
381(9)
A.3.1 Descriptive Statistics
381(2)
A.3.2 Normal Distribution
383(1)
A.3.3 x2-Distribution
383(3)
A.3.4 t-Distribution
386(1)
A.3.5 F-Distribution
386(4)
A.4 Excel for Chemometrics
390(18)
A.4.1 Names and Addresses
390(4)
A.4.2 Equations and Functions
394(4)
A.4.3 Add-Ins
398(1)
A.4.4 Charts
398(2)
A.4.5 Downloadable Macros
400(8)
A.5 Matlab for Chemometrics
408(21)
A.5.1 Getting Started
408(1)
A.5.2 File Types
409(2)
A.5.3 Matrices
411(5)
A.5.4 Importing and Exporting Data
416(1)
A.5.5 Introduction to Programming and Structure
417(1)
A.5.6 Graphics
418(11)
Answers to the Multiple Choice Questions 429(4)
Index 433
RICHARD G. BRERETON is Director of Brereton Consultancy and Emeritus Professor at the University of Bristol, UK. He is Fellow of the Royal Society of Chemistry, Royal Statistical Society and Royal Society of Medicine. He has applied chemometrics in a wide variety of areas including pharmaceuticals, materials, metabolomics, heritage studies and forensics, and has published over 400 articles, including writing/editing eight books.