Chemometrics

The third edition of this long-selling introductory textbook and ready reference covers all pertinent topics, from basic statistics via modeling and databases right up to the latest regulatory issues.
The experienced and internationally recognized author, Matthias Otto, introduces the statistical-mathematical evaluation of chemical measurements, especially analytical ones, going on to provide a modern approach to signal processing, designing and optimizing experiments, pattern recognition and classification, as well as modeling simple and nonlinear relationships. Analytical databases are equally covered as are applications of multiway analysis, artificial intelligence, fuzzy theory, neural networks, and genetic algorithms. The new edition has 10% new content to cover such recent developments as orthogonal signal correction and new data exchange formats, tree based classification and regression, independent component analysis, ensemble methods and neuro-fuzzy systems. It still retains, however, the proven features from previous editions: worked examples, questions and problems, additional information and brief explanations in the margin.

Since 2016, Matthias Otto is Professor emeritus of Analytical Chemistry at the TU Bergakademie Freiberg in Germany. He was born in Dresden (Germany) and did all entire studies at the University of Leipzig. In 1984 he moved to Freiberg (Germany) as a lecturer and was appointed full Professor there in 1987. He has taught almost all aspects of Analytical Chemistry, mainly within the curricula of chemistry, applied sciences and geoecology, and organizes courses in basic and advanced chemometrics. He is author and editor of successful textbooks in analytical chemistry.

List of Abbreviations VII

Symbols IX

1 What is Chemometrics? 1

1.1 The Computer-Based Laboratory 2

1.2 Statistics and Data Interpretation 10

1.3 Computer-Based Information Systems/Artificial Intelligence 11

General Reading 12

Questions and Problems 13

2 Basic Statistics 15

2.1 Descriptive Statistics 16

2.2 Statistical Tests 28

2.3 Analysis of Variance 44

General Reading 50

Questions and Problems 52

3 Signal Processing and Time Series Analysis 55

3.1 Signal Processing 56

3.2 Time Series Analysis 83

General Reading 90

Questions and Problems 91

4 Optimization and Experimental Design 93

4.1 Systematic Optimization 94

4.2 Objective Functions and Factors 95

4.3 Experimental Design and Response Surface Methods 102

4.4 Sequential Optimization: Simplex Method 125

General Reading 132

Questions and Problems 133

5 Pattern Recognition and Classification 135

5.1 Preprocessing of Data 137

5.2 Unsupervised Methods 140

5.3 Supervised Methods 184

General Reading 209

Questions and Problems 210

6 Modeling 213

6.1 Univariate Linear Regression 214

6.2 Multiple Linear Regression 231

6.3 Nonlinear Methods 258

General Reading 269

Questions and Problems 271

7 Analytical Databases 273

7.1 Representation of Analytical Information 274

7.2 Library Search 286

7.3 Simulation of Spectra 292

General Reading 294

Questions and Problems 295

8 Knowledge Processing and Soft Computing 297

8.1 Artificial Intelligence and Expert Systems 297

8.2 Neural Networks 306

8.3 Fuzzy Theory 321

8.4 Genetic Algorithms and Other Global Search Strategies 334

General Reading 342

Questions and Problems 344

9 Quality Assurance and Good Laboratory Practice 345

9.1 Validation and Quality Control 346

9.2 Accreditation and Good Laboratory Practice 351

General Reading 352

Questions and Problems 353

Appendix 355

Index 371

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Summary

Author Biography

Table of Contents

Supplemental Materials