Matthias Otto

Chemometrics (eBook, PDF)

Statistics and Computer Application in Analytical Chemistry

• Format: PDF

Produktbeschreibung

Serving as both an introductory text and practical approach, covers all relevant chemometric topics from basic statistics, via modeling and databases right up to the latest artificial intelligence and neural network developments.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Wiley-VCH
• Seitenzahl: 432
• Erscheinungstermin: 22. November 2023
• Englisch
• ISBN-13: 9783527843787
• Artikelnr.: 69529035

Autorenporträt

Matthias Otto is Professor emeritus of Analytical Chemistry at the TU Bergakademie Freiberg in Germany. He studied 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.

Inhaltsangabe

Contents

List of Abbreviations VII

Symbols IX

1 What is Chemometrics?
1.1 The Computer-Based Laboratory
1.2 Statistics and Data Interpretation
1.3 Computer-Based Information Systems/Artificial Intelligence
General Reading
Questions and Problems

2 Basic Statistics
2.1 Descriptive Statistics
2.2 Statistical Tests
2.3 Analysis of Variance
General Reading
Questions and Problems

3 Signal Processing and Time Series Analysis
3.1 Signal Processing
3.2 Time Series Analysis
General Reading
Questions and Problems

4 Optimization and Experimental Design
4.1 Systematic Optimization
4.2 Objective Functions and Factors
4.3 Experimental Design and Response Surface Methods
4.4 Sequential Optimization: Simplex Method
General Reading
Questions and Problems

5 Pattern Recognition and Classification
5.1 Preprocessing of Data
5.2 Unsupervised Methods
5.3 Supervised Methods
General Reading
Questions and Problems

6 Modeling
6.1 Univariate Linear Regression
6.2 Multiple Linear Regression
6.3 Nonlinear Methods
General Reading
Questions and Problems

7 Analytical Databases
7.1 Representation of Analytical Information
7.2 Library Search
7.3 Simulation of Spectra
General Reading
Questions and Problems

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
General Reading
Questions and Problems

9 Quality Assurance and Good Laboratory Practice
9.1 Validation and Quality Control
9.2 Accreditation and Good Laboratory Practice
General Reading
Questions and Problems
Appendix
Index