Chemometrics : statistics and computer application in analytical chemistry
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Bibliographic Information
Chemometrics : statistics and computer application in analytical chemistry
Wiley-VCH-Verl, c2007
2., completely rev. and enl. ed
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Literaturangaben
Chemometrie
Description and Table of Contents
Description
This new edition contains 10 percent more worked examples as well as the addition of modern chemometric developments, such as support vector machines, wavelet transformations and multi-way analysis. With its inclusion of statistics, fuzzy theory, databases, and quality assurance, this remains the textbook with the broadest coverage, and retains such proven features as additional information provided in the margin, a glossary of terms plus an overview of suitable chemometric software. While ideal for students of chemistry, pharmacy, biochemistry and ecology, this is equally useful for scientists in industry and research institutes.
Table of Contents
1 What is Chemometrics? 1.1 The Computer-based Laboratory. 1.2 Statistics and Data Interpretation. 1.3 Computer-based Information Systems/Artificial Intelligence. 1.4 General Reading. 2 Basic Statistics. 2.1 Descriptive Statistics. 2.2 Statistical Tests. 2.3 Analysis of Variance. 2.4 General Reading. 3 Signal Processing and Time-Series Analysis. 3.1 Signal Processing. 3.2 Times Series Analysis. 3.3 General Reading. 4 Optimization and Experimental Design. 4.1 Objective Functions and Factors. 4.2 Experimental Designs and Response Surface Methods. 4.2.1 Fundamentals. 4.2.2 Two-level designs: screening designs. 4.2.3 Three-level designs: response surface designs. 4.3 Sequential Optimization: Simplex Method. 4.4 General Reading. 5 Pattern Recognition and Classification. 5.1 Preprocessing of Data. 5.2 Unsupervised Methods. 5.2.1 Factorial methods. 5.2.2 Cluster analysis. 5.2.3 Graphical methods. 5.3 Supervised Methods. 5.3.1 Linear learning machine. 5.3.2 Discriminant analysis. 5.3.3 -nearest neighbor method. 5.3.4 SIMCA. 5.3.5 Support vector machines. 5.4 General Reading. 6 Modeling. 6.1 Univariate Linear Regression. 6.2 Multiple Linear Regression. 6.2.1 Ordinary test squares regression. 6.2.2 Biased parameter estimations: PCR and PLS. 6.2.3 Applications for multicomponent analysis. 6.2.4 Regression diagnostics. 6.2.5 Multiway regression (modeling). 6.3 Nonlinear Methods. 6.3.1 Nonlinear regression analysis. 6.3.2 Nonparametric methods. 6.4 General Reading. 7 Analytical Databases. 7.1 Representation of Analytical Information. 7.2 Library Search. 7.3 General Reading. 8 Knowledge Processing and Soft Computing. 8.1 Artificial Intelligence and Expert Systems. 8.2 Neural Networks. 8.3 Fuzzy Theory. 8.4 Genetic Algorithms and Other Global Search Strategies. 8.5 General Reading. 9 Quality Assurance and Good Laboratory Practice. 9.1 Validation and Quality Control. 9.2 Accreditation and Good Laboratory Practice. 9.3 General Reading. Appendix. Statistical Distributions. Digital filters. Experimental Designs. Matrix Algebra. Software. Index.
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