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by Tormod Næs, Tomas Isaksson, Tom Fearn and Tony Davies
This important new book presents these topics in an accessible way, yet provides sufficient depth to be of real practical value.
This GUIDE to two of the most important area of chemometrics discusses the following topics:
Calibration
- the non-selectivity problem
- the collinearity problem
- the non-linearity problem
- calibration data selection
- the outlier problem
Classification
- cluster analysis
- discrimination analysis
The aim has been to produce a readable text, for non-mathematicians, as an introduction to these areas for people with little or moderate knowledge of chemometrics. The book has been designed in an attractive and easily read format, with many diagrams and the use of margin notes to highlight important features.
The Authors
The authors are all very well known in this field and they bring a complementary range of experience and interests to this project.
Tormod Næs has a PhD in statistics and is Professor at University of Oslo and Principal research scientist at Matforsk. His main interest is developing and applying multivariate methods.
Tomas Isaksson is Professor of Food Science at Agricultural University of Norway. He has worked in the area of non-destructive quality analysis and chemometrics for about 15 years.
Tom Fearn is Professor of Applied Statistics at University College London. He has worked in the area of multivariate calibration and discrimination for over twenty years.
Tony (A.M.C.) Davies first used multiple regression analysis in 1965. He works as an NIR consultant and editor and writes about chemometrics in the “Tony Davies Column” in Spectroscopy Europe.
Hardback
Published: 2002
Pages: viii + 344
Contents
Basic problems and their solutions
Univariate calibration and the need for multivariate methods
Multicollinearity and the need for data compression
Data compression by PCR and PLS
Interpreting PCR and PLS solutions
Data compression by variable selection
Data compression by Fourier analysis and wavelets
Non-linearity problems in calibration
Scatter correction of spectroscopic data
The idea behind and algorithm for locally weighted regression
Other methods used to solve non-linearity problems
Validation
Outlier detection
Selection of samples for calibration
Monitoring calibration equations
Standardisation of instruments
Qualitative analysis/classification
Abbreviations and symbols
Appendix A. Technical details
Appendix B. NIR spectroscopy
Appendix C. Proposed calibration and classification procedures
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