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.

1;Cover;1 2;Title Page;5 3;Copyright;6 4;Contents;7 5;List of Abbreviations;9 6;Symbols;11 7;Chapter 1 What is Chemometrics?;13 7.1;1.1 The Computer-Based Laboratory;14 7.2;1.2 Statistics and Data Interpretation;22 7.3;1.3 Computer-Based Information Systems/Artificial Intelligence;23 7.4;General Reading;24 7.5;Questions and Problems;25 8;Chapter 2 Basic Statistics;27 8.1;2.1 Descriptive Statistics;28 8.2;2.2 Statistical Tests;40 8.3;2.3 Analysis of Variance;56 8.4;General Reading;62 8.5;Questions and Problems;64 9;Chapter 3 Signal Processing and Time Series Analysis;67 9.1;3.1 Signal Processing;68 9.2;3.2 Time Series Analysis;95 9.3;General Reading;102 9.4;Questions and Problems;103 10;Chapter 4 Optimization and Experimental Design;105 10.1;4.1 Systematic Optimization;106 10.2;4.2 Objective Functions and Factors;107 10.3;4.3 Experimental Design and Response Surface Methods;114 10.4;4.4 Sequential Optimization: Simplex Method;137 10.5;General Reading;144 10.6;Questions and Problems;145 11;Chapter 5 Pattern Recognition and Classification;147 11.1;5.1 Preprocessing of Data;149 11.2;5.2 Unsupervised Methods;152 11.3;5.3 Supervised Methods;196 11.4;General Reading;221 11.5;Questions and Problems;222 12;Chapter 6 Modeling;225 12.1;6.1 Univariate Linear Regression;226 12.2;6.2 Multiple Linear Regression;243 12.3;6.3 Nonlinear Methods;270 12.4;General Reading;281 12.5;Questions and Problems;283 13;Chapter 7 Analytical Databases;285 13.1;7.1 Representation of Analytical Information;286 13.2;7.2 Library Search;298 13.3;7.3 Simulation of Spectra;304 13.4;General Reading;306 13.5;Questions and Problems;307 14;Chapter 8 Knowledge Processing and Soft Computing;309 14.1;8.1 Artificial Intelligence and Expert Systems;309 14.2;8.2 Neural Networks;318 14.3;8.3 Fuzzy Theory;333 14.4;8.4 Genetic Algorithms and Other Global Search Strategies;346 14.5;General Reading;354 14.6;Questions and Problems;356 15;Chapter 9 Quality Assurance and Good Laboratory Practice;357 15.1;9.1 Validation and Quality Control;358 15.2;9.2 Accreditation and Good Laboratory Practice;363 15.3;General Reading;364 15.4;Questions and Problems;365 16;Appendix;367 17;Index;383 18;EULA;396