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Chemometrics in Excel

ISBN: 978-1-119-02776-8
336 pages
November 2014
Chemometrics in Excel (1119027764) cover image

Description

Providing an easy explanation of the fundamentals, methods, and applications of chemometrics

• Acts as a practical guide to multivariate data analysis techniques
• Explains the methods used in Chemometrics and teaches the reader to perform all relevant calculations
• Presents the basic chemometric methods as worksheet functions in Excel
• Includes Chemometrics Add In for download which uses Microsoft Excel® for chemometrics training
• Online downloads includes workbooks with examples

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Table of Contents

Preface xvii

PART I INTRODUCTION 1

1 What is Chemometrics? 3

1.1 Subject of Chemometrics, 3

1.2 Historical Digression, 5

2 What the Book Is About? 8

2.1 Useful Hints, 8

2.2 Book Syllabus, 9

2.3 Notations, 10

3 Installation of Chemometrics Add-In 11

3.1 Installation, 11

3.2 General Information, 14

4 Further Reading on Chemometrics 15

4.1 Books, 15

4.1.1 The Basics, 15

4.1.2 Chemometrics, 16

4.1.3 Supplements, 16

4.2 The Internet, 17

4.2.1 Tutorials, 17

4.3 Journals, 17

4.3.1 Chemometrics, 17

4.3.2 Analytical, 18

4.3.3 Mathematical, 18

4.4 Software, 18

4.4.1 Specialized Packages, 18

4.4.2 General Statistic Packages, 19

4.4.3 Free Ware, 19

PART II THE BASICS 21

5 Matrices and Vectors 23

5.1 The Basics, 23

5.1.1 Matrix, 23

5.1.2 Simple Matrix Operations, 24

5.1.3 Matrices Multiplication, 25

5.1.4 Square Matrix, 26

5.1.5 Trace and Determinant, 27

5.1.6 Vectors, 28

5.1.7 Simple Vector Operations, 29

5.1.8 Vector Products, 29

5.1.9 Vector Norm, 30

5.1.10 Angle Between Vectors, 30

5.1.11 Vector Representation of a Matrix, 30

5.1.12 Linearly Dependent Vectors, 31

5.1.13 Matrix Rank, 31

5.1.14 Inverse Matrix, 31

5.1.15 Pseudoinverse, 32

5.1.16 Matrix–Vector Product, 33

5.2 Advanced Information, 33

5.2.1 Systems of Linear Equations, 33

5.2.2 Bilinear and Quadratic Forms, 34

5.2.3 Positive Definite Matrix, 34

5.2.4 Cholesky Decomposition, 34

5.2.5 Polar Decomposition, 34

5.2.6 Eigenvalues and Eigenvectors, 35

5.2.7 Eigenvalues, 35

5.2.8 Eigenvectors, 35

5.2.9 Equivalence and Similarity, 36

5.2.10 Diagonalization, 37

5.2.11 Singular Value Decomposition (SVD), 37

5.2.12 Vector Space, 38

5.2.13 Space Basis, 39

5.2.14 Geometric Interpretation, 39

5.2.15 Nonuniqueness of Basis, 39

5.2.16 Subspace, 40

5.2.17 Projection, 40

6 Statistics 42

6.1 The Basics, 42

6.1.1 Probability, 42

6.1.2 Random Value, 43

6.1.3 Distribution Function, 43

6.1.4 Mathematical Expectation, 44

6.1.5 Variance and Standard Deviation, 44

6.1.6 Moments, 44

6.1.7 Quantiles, 45

6.1.8 Multivariate Distributions, 45

6.1.9 Covariance and Correlation, 45

6.1.10 Function, 46

6.1.11 Standardization, 46

6.2 Main Distributions, 46

6.2.1 Binomial Distribution, 46

6.2.2 Uniform Distribution, 47

6.2.3 Normal Distribution, 48

6.2.4 Chi-Squared Distribution, 50

6.2.5 Student’s Distribution, 52

6.2.6 F-Distribution, 53

6.2.7 Multivariate Normal Distribution, 54

6.2.8 Pseudorandom Numbers, 55

6.3 Parameter Estimation, 56

6.3.1 Sample, 56

6.3.2 Outliers and Extremes, 56

6.3.3 Statistical Population, 56

6.3.4 Statistics, 57

6.3.5 Sample Mean and Variance, 57

6.3.6 Sample Covariance and Correlation, 58

6.3.7 Order Statistics, 59

6.3.8 Empirical Distribution and Histogram, 60

6.3.9 Method of Moments, 61

6.3.10 The Maximum Likelihood Method, 62

6.4 Properties of the Estimators, 62

6.4.1 Consistency, 62

6.4.2 Bias, 63

6.4.3 Effectiveness, 63

6.4.4 Robustness, 63

6.4.5 Normal Sample, 64

6.5 Confidence Estimation, 64

6.5.1 Confidence Region, 64

6.5.2 Confidence Interval, 65

6.5.3 Example of a Confidence Interval, 65

6.5.4 Confidence Intervals for the Normal Distribution, 65

6.6 Hypothesis Testing, 66

6.6.1 Hypothesis, 66

6.6.2 Hypothesis Testing, 66

6.6.3 Type I and Type II Errors, 67

6.6.4 Example, 67

6.6.5 Pearson’s Chi-Squared Test, 67

6.6.6 F-Test, 69

6.7 Regression, 70

6.7.1 Simple Regression, 70

6.7.2 The Least Squares Method, 71

6.7.3 Multiple Regression, 72

Conclusion, 73

7 Matrix Calculations in Excel 74

7.1 Basic Information, 74

7.1.1 Region and Language, 74

7.1.2 Workbook, Worksheet, and Cell, 76

7.1.3 Addressing, 77

7.1.4 Range, 78

7.1.5 Simple Calculations, 78

7.1.6 Functions, 78

7.1.7 Important Functions, 81

7.1.8 Errors in Formulas, 85

7.1.9 Formula Dragging, 86

7.1.10 Create a Chart, 87

7.2 Matrix Operations, 88

7.2.1 Array Formulas, 88

7.2.2 Creating and Editing an Array Formula, 90

7.2.3 Simplest Matrix Operations, 91

7.2.4 Access to the Part of a Matrix, 91

7.2.5 Unary Operations, 93

7.2.6 Binary Operations, 95

7.2.7 Regression, 95

7.2.8 Critical Bug in Excel 2003, 99

7.2.9 Virtual Array, 99

7.3 Extension of Excel Possibilities, 100

7.3.1 VBA Programming, 100

7.3.2 Example, 101

7.3.3 Macro Example, 103

7.3.4 User-Defined Function Example, 104

7.3.5 Add-Ins, 105

7.3.6 Add-In Installation, 106

Conclusion, 107

8 Projection Methods in Excel 108

8.1 Projection Methods, 108

8.1.1 Concept and Notation, 108

8.1.2 PCA, 109

8.1.3 PLS, 110

8.1.4 Data Preprocessing, 111

8.1.5 Didactic Example, 112

8.2 Application of Chemometrics Add-In, 113

8.2.1 Installation, 113

8.2.2 General, 113

8.3 PCA, 114

8.3.1 ScoresPCA, 114

8.3.2 LoadingsPCA, 114

8.4 PLS, 116

8.4.1 ScoresPLS, 116

8.4.2 UScoresPLS, 117

8.4.3 LoadingsPLS, 118

8.4.4 WLoadingsPLS, 119

8.4.5 QLoadingsPLS, 120

8.5 PLS2, 121

8.5.1 ScoresPLS2, 121

8.5.2 UScoresPLS2, 122

8.5.3 LoadingsPLS2, 124

8.5.4 WLoadingsPLS2, 125

8.5.5 QLoadingsPLS2, 126

8.6 Additional Functions, 127

8.6.1 MIdent, 127

8.6.2 MIdentD2, 127

8.6.3 MCutRows, 129

8.6.4 MTrace, 129

Conclusion, 130

PART IIICHEMOMETRICS 131

9 Principal Component Analysis (PCA) 133

9.1 The Basics, 133

9.1.1 Data, 133

9.1.2 Intuitive Approach, 134

9.1.3 Dimensionality Reduction, 136

9.2 Principal Component Analysis, 136

9.2.1 Formal Specifications, 136

9.2.2 Algorithm, 137

9.2.3 PCA and SVD, 137

9.2.4 Scores, 138

9.2.5 Loadings, 139

9.2.6 Data of Special Kind, 140

9.2.7 Errors, 140

9.2.8 Validation, 143

9.2.9 Decomposition “Quality”, 143

9.2.10 Number of Principal Components, 144

9.2.11 The Ambiguity of PCA, 145

9.2.12 Data Preprocessing, 146

9.2.13 Leverage and Deviation, 146

9.3 People and Countries, 146

9.3.1 Example, 146

9.3.2 Data, 147

9.3.3 Data Exploration, 147

9.3.4 Data Pretreatment, 148

9.3.5 Scores and Loadings Calculation, 149

9.3.6 Scores Plots, 151

9.3.7 Loadings Plot, 152

9.3.8 Analysis of Residuals, 153

Conclusion, 153

10 Calibration 156

10.1 The Basics, 156

10.1.1 Problem Statement, 156

10.1.2 Linear and Nonlinear Calibration, 157

10.1.3 Calibration and Validation, 158

10.1.4 Calibration “Quality”, 160

10.1.5 Uncertainty, Precision, and Accuracy, 162

10.1.6 Underfitting and Overfitting, 163

10.1.7 Multicollinearity, 164

10.1.8 Data Preprocessing, 166

10.2 Simulated Data, 166

10.2.1 The Principle of Linearity, 166

10.2.2 “Pure” Spectra, 166

10.2.3 “Standard” Samples, 166

10.2.4 X Data Creation, 167

10.2.5 Data Centering, 168

10.2.6 Data Overview, 168

10.3 Classic Calibration, 169

10.3.1 Univariate (Single Channel) Calibration, 169

10.3.2 The Vierordt Method, 172

10.3.3 Indirect Calibration, 174

10.4 Inverse Calibration, 176

10.4.1 Multiple Linear Calibration, 177

10.4.2 Stepwise Calibration, 178

10.5 Latent Variables Calibration, 180

10.5.1 Projection Methods, 180

10.5.2 Latent Variables Regression, 184

10.5.3 Implementation of Latent Variable Calibration, 185

10.5.4 Principal Component Regression (PCR), 186

10.5.5 Projection on the Latent Structures-1 (PLS1), 188

10.5.6 Projection on the Latent Structures-2 (PLS2), 191

10.6 Methods Comparison, 193

Conclusion, 197

11 Classification 198

11.1 The Basics, 198

11.1.1 Problem Statement, 198

11.1.2 Types of Classes, 199

11.1.3 Hypothesis Testing, 199

11.1.4 Errors in Classification, 200

11.1.5 One-Class Classification, 200

11.1.6 Training and Validation, 201

11.1.7 Supervised and Unsupervised Training, 201

11.1.8 The Curse of Dimensionality, 201

11.1.9 Data Preprocessing, 201

11.2 Data, 202

11.2.1 Example, 202

11.2.2 Data Subsets, 203

11.2.3 Workbook Iris.xls, 204

11.2.4 Principal Component Analysis, 205

11.3 Supervised Classification, 205

11.3.1 Linear Discriminant Analysis (LDA), 205

11.3.2 Quadratic Discriminant Analysis (QDA), 210

11.3.3 PLS Discriminant Analysis (PLSDA), 214

11.3.4 SIMCA, 217

11.3.5 k-Nearest Neighbors (kNN), 223

11.4 Unsupervised Classification, 225

11.4.1 PCA Again (Revisited), 225

11.4.2 Clustering by K-Means, 225

Conclusion, 229

12 Multivariate Curve Resolution 230

12.1 The Basics, 230

12.1.1 Problem Statement, 230

12.1.2 Solution Ambiguity, 232

12.1.3 Solvability Conditions, 234

12.1.4 Two Types of Data, 235

12.1.5 Known Spectrum or Profile, 236

12.1.6 Principal Component Analysis (PCA), 236

12.1.7 PCA and MCR, 237

12.2 Simulated Data, 237

12.2.1 Example, 237

12.2.2 Data, 238

12.2.3 PCA, 238

12.2.4 The HELP Plot, 240

12.3 Factor Analysis, 241

12.3.1 Procrustes Analysis, 241

12.3.2 Evolving Factor Analysis (EFA), 244

12.3.3 Windows Factor Analysis (WFA), 246

12.4 Iterative Methods, 249

12.4.1 Iterative Target Transform Factor Analysis (ITTFA), 249

12.4.2 Alternating Least Squares (ALS), 250

Conclusion, 252

PART IV SUPPLEMENTS 255

13 Extension Of Chemometrics Add-In 257

13.1 Using Virtual Arrays, 257

13.1.1 Simulated Data, 257

13.1.2 Virtual Array, 259

13.1.3 Data Preprocessing, 259

13.1.4 Decomposition, 260

13.1.5 Residuals Calculation, 260

13.1.6 Eigenvalues Calculation, 262

13.1.7 Orthogonal Distances Calculation, 263

13.1.8 Leverages Calculation, 264

13.2 Using VBA Programming, 265

13.2.1 VBA Advantages, 265

13.2.2 Virtualization of Real Arrays, 265

13.2.3 Data Preprocessing, 266

13.2.4 Residuals Calculation, 267

13.2.5 Eigenvalues Calculation, 268

13.2.6 Orthogonal Distances Calculation, 269

13.2.7 Leverages Calculation, 270

Conclusion, 271

14 Kinetic Modeling of Spectral Data 272

14.1 The “Grey” Modeling Method, 272

14.1.1 Problem Statement, 272

14.1.2 Example, 274

14.1.3 Data, 274

14.1.4 Soft Method of Alternating Least Squares (Soft-ALS), 275

14.1.5 Hard Method of Alternating Least Squares (Hard-ALS), 277

14.1.6 Using Solver Add-In, 279

Conclusions, 282

15 MATLAB®: Beginner’s Guide 283

15.1 The Basics, 283

15.1.1 Workspace, 283

15.1.2 Basic Calculations, 285

15.1.3 Echo, 285

15.1.4 Workspace Saving: MAT-Files, 286

15.1.5 Diary, 286

15.1.6 Help, 287

15.2 Matrices, 287

15.2.1 Scalars, Vectors, and Matrices, 287

15.2.2 Accessing Matrix Elements, 289

15.2.3 Basic Matrix Operations, 289

15.2.4 Special Matrices, 290

15.2.5 Matrix Calculations, 292

15.3 Integrating Excel and MATLAB®, 294

15.3.1 Configuring Excel, 294

15.3.2 Data Exchange, 294

15.4 Programming, 295

15.4.1 M-Files, 295

15.4.2 Script File, 296

15.4.3 Function File, 297

15.4.4 Plotting, 298

15.4.5 Plot Printing, 300

15.5 Sample Programs, 301

15.5.1 Centering and Scaling, 301

15.5.2 SVD/PCA, 301

15.5.3 PCA/NIPALS, 302

15.5.4 PLS1, 303

15.5.5 PLS2, 304

Conclusion, 306

Afterword. The Fourth Paradigm 307

Index 311

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Author Information

Alexey L Pomerantsev is a Leading Researcher at The Russian Academy of Science. He is a founding member and Chair of the Russian Chemometrics Society, being instrumental in organizing the annual Winter Symposium on Chemometrics. He is a peer reviewer and member of Editorial Board of the Journal ‘Chemometrics and Intelligent Laboratory Systems.’ Dr. Pomerantsev has over 100 publications, many of them dealing with Chemometric Investigations.
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Reviews

“The book is for sure very interesting and very well written, and it covers all the major topics of chemometrics.”  (Journal of Chemometrics, 14 July 2015)

 

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