Chemometrics A Practical Guide

Complete with helpful chapter-end summaries, technical references, and more, this book is an invaluable hands-on resource for analytical chemists and laboratory scientists who use chemometrics in their work.

KENNETH R. BEEBE is a global technical manager with The Dow Chemical Company in Freeport, Texas.<br> <br> J. PELL and MARY BETH SEASHOLTZ are chemometricians in the Process Analytical Research and Development group with The Dow Chemical Company in Midland, Michigan.

Preface

xi

1 The Six Habits of an Effective Chemometrician

1

(8)

1.1 Using this Book

2

(1)

1.2 The Six Habits of an Effective Chemometrician

3

(2)

1.3 Definitions

5

(3)

1.4 Summary of the Six Habits of an Effective Chemometrician

8

(1)

Reference

8

(1)

2 Defining the Problem

9

(17)

2.1 Decision Tree

9

(2)

2.2 Communication, Communication, Communication

11

(5)

2.2.1 Understanding the System

11

(1)

2.2.2 Understanding the Problem

12

(4)

2.2.3 Understanding the Chemometrics View

16

(1)

2.3 Experimental Planning

16

(7)

2.3.1 Benefits of Experimental Planning and Design

17

(1)

2.3.2 Steps for Designing the Experiment

18

(5)

2.4 Summary of Defining the Problem

23

(1)

References

23

(1)

Appendix A. Special Considerations for Inverse Chemometric Modeling

23

(3)

3 Preprocessing

26

(30)

3.1 Preprocessing the Samples

26

(21)

3.1.1 Normalization

27

(2)

3.1.2 Sample Weighting

29

(1)

3.1.3 Smoothing

30

(6)

3.1.3.1 Mean Smoother

30

(1)

3.1.3.2 Running Mean Smoother

30

(1)

3.1.3.3 Running Median Smoother

31

(1)

3.1.3.4 Running Polynomial Smoother

32

(3)

3.1.3.5 Fourier Filter Smoother

35

(1)

3.1.4 Baseline Corrections

36

(11)

3.1.4.1 Explicit Modeling Approach

36

(5)

3.1.4.2 Derivatives

41

(5)

3.1.4.3 Multiplicative Scatter Correction

46

(1)

3.2 Preprocessing the Variables

47

(6)

3.2.1 Mean Centering

48

(2)

3.2.2 Variable Weighting

50

(3)

3.2.2.1 A Priori Information Weighting

50

(1)

3.2.2.2 Variable Selection

50

(1)

3.2.2.3 Variance Scaling

51

(1)

3.2.2.4 Autoscaling

52

(1)

3.3 Summary of Preprocessing

53

(2)

References

55

(1)

4 Pattern Recognition

56

(127)

4.1 Decision Tree

62

(2)

4.2 Unsupervised Learning

64

(48)

4.2.1 Hierarchical Cluster Analysis (HCA)

65

(16)

4.2.1.1 HCA Example 1

71

(5)

4.2.1.2 HCA Example 2

76

(5)

4.2.2 Principal Components Analysis (PCA)

81

(30)

4.2.2.1 PCA Example 1

87

(12)

4.2.2.2 PCA Example 2

99

(12)

4.2.3 Summary of Unsupervised Pattern Recognition

111

(1)

4.3 Supervised Learning

112

(69)

4.3.1 K-Nearest Neighbor (KNN)

113

(21)

4.3.1.1 KNN Example 1

116

(9)

4.3.1.2 KNN Example 2

125

(9)

4.3.2 SIMCA (Soft Independent Modeling of Class Analogies)

134

(46)

4.3.2.1 SIMCA Example 1

138

(26)

4.3.2.2 SIMCA Example 2

164

(16)

4.3.3 Summary of Supervised Pattern Recognition Methods

180

(1)

4.4 Summary of Pattern Recognition

181

(1)

References

182

(1)

5 Multivariate Calibration and Prediction

183

(158)

5.1 Decision Tree

186

(2)

5.2 Classical Methods

188

(57)

5.2.1 Direct CLS

192

(26)

5.2.1.1 DCLS Example 1

192

(18)

5.2.1.2 DCLS Example 2

210

(8)

5.2.2 Indirect CLS

218

(25)

5.2.2.1 ICLS Example 1

218

(9)

5.2.2.2 ICLS Example 2

227

(16)

5.2.3 Summary of Classical Methods

243

(2)

5.3 Inverse Methods

245

(92)

5.3.1 Multiple Linear Regression (MLR)

250

(28)

5.3.1.1 MLR Example 1

251

(11)

5.3.1.2 MLR Example 2

269

(9)

5.3.2 Partial Least Squares/Principal Components Regression

278

(57)

5.3.2.1 PLS Example 1

284

(31)

5.3.2.2 PLS Example 2

315

(12)

5.3.2.3 PLS Example 3

327

(8)

5.3.3 Summary of Inverse Methods

335

(2)

5.4 Summary of Multivariate Calibration and Prediction

337

(1)

References

338

(3)

Index

341

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Chemometrics A Practical Guide

Summary

Author Biography

Table of Contents

Supplemental Materials