Chemometrics : statistics and computer application in analytical chemistry

Chemometrics - easy to understand!
Chemometrics are becoming more and more important for the evaluation of analytical data. Work in a modern lab is almost unthinkable without the use of statistical methods and computer-based data analysis. This new textbook is especially suited to give you a comprehensive overview of all areas of chemometrics.
Based on the most important fundamentals of statistics, the author provides a modern approach to signal processing, planning and optimizing experiments, pattern recognition and classification as well as modeling simple and nonlinear relationships. Analytical databases are covered as well as applications of artificial intelligence, fuzzy theory, neuronal networks, or genetic algorithms. Numerous worked examples and helpful comments in the margin make reading and understanding easy.
Students of chemistry, pharmacy, biochemistry, or ecology will find everything they need to know about chemometrics. Also scientists in industry and research will find this book a useful introduction.
Matthias Otto has offered courses on chemometrices for many years and has held lectures at leading universities.

Reviews (1)

Choice Review

Otto describes his book as a text on chemometrics derived from his lectures to undergraduate and graduate students, presumably of chemistry. He also suggests that the book would be suitable for individual study by various professionals as well. The book is an English translation from the German. As such, there are some awkward sentences and phrases and typographical errors/misspellings. The book is very concise and covers a great deal of ground in a short amount of space. It is definitely not a comprehensive primer on chemometrics, statistics, analysis, or knowledge processing. Otto recognizes that many chemists are not well grounded in statistical methods; thus, he provides a quick primer on descriptive and inferential statistics. separate chapters cover a number of popular methods of chemometrics, including experimental design, signal processing, pattern recognition, data banks, and knowledge processing. He finishes with a chapter on quality assurance and good lab practice. Reader understanding is aided by a set of learning objectives and questions/problems. A valuable overview of chemometrics, but definitely not for neophytes. Upper-division undergraduates through professionals. J. A. Siegel; Michigan State University

What is Chemometrics?

Basic Statistics

Signal Processing and Time-Series Analysis

Optimization and Experimental Design

Pattern Recognition and Classification

Modeling

Analytical Databanks

Knowledge Processing and Soft Computing

Quality Assurance and Good Laboratory Practice

Appendix

Index

Mevcut:*

Bound With These Titles

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Table of Contents