Practical guide to chemometrics
著者
書誌事項
Practical guide to chemometrics
Taylor&Francis, c2006
2nd ed
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
The limited coverage of data analysis and statistics offered in most undergraduate and graduate analytical chemistry courses is usually focused on practical aspects of univariate methods. Drawing in real-world examples, Practical Guide to Chemometrics, Second Edition offers an accessible introduction to application-oriented multivariate methods of data analysis and procedures that are highly beneficial to solving a variety of problems using analytical chemistry and statistics.
Rather than overshadowing the concepts with theoretical background, this book uses application-oriented examples to illustrate how chemometrics techniques can be applied to complex scenarios with multiple and dynamic variables. The book presents a diverse selection of topics that include sampling, modeling, experimental design, calibration, pattern recognition, data analysis techniques, algorithms, and error. This second edition has been completely revised to feature new chapters on principal component analysis, self-modeling curve resolution, and multi-way analysis methods. It includes expanded material on normal distributions, sampling theory, signal processing, and digital filtering.
Embracing the growing role of chemometrics in some of the latest research trends, such as quantitative biology, bioinformatics, and proteomics, this book also identifies several areas for future development and applications. Practical Guide to Chemometrics, Second Edition continues to offer a reliable source of useful information in a style that is accessible to all levels of students, professionals, and researchers involved in analyzing scientific data.
目次
Introduction to Chemometrics. Statistical Evaluation of Data. Sampling Theory, Distribution Functions, and the Multivariate Normal Distribution. Principal Component Analysis. Calibration. Robust Calibration. Kinetic Modeling of Multivariate Measurements with Nonlinear Regression. Response-Surface Modeling and Experimental Design. Classification and Pattern Recognition. Signal Processing and Digital Filtering. Multivariate Curve Resolution. Three-Way Calibration with Hyphenated Data. Future Trends in Chemometrics.Index
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