Multivariate Pattern Recognition in Chemometrics
Illustrated by Case StudiesEdited By
- R.G. Brereton, University of Bristol, Bristol, UK
Chemometrics originated from multivariate statistics in chemistry, and this field is still the core of the subject. The increasing availability of user-friendly software in the laboratory has prompted the need to optimize it safely. This work comprises material presented in courses organized from 1987-1992, aimed mainly at professionals in industry.
The book covers approaches for pattern recognition as applied, primarily, to multivariate chemical data. These include data reduction and display techniques, principal components analysis and methods for classification and clustering.
Comprehensive case studies illustrate the book, including numerical examples, and extensive problems are interspersed throughout the text. The book contains extensive cross-referencing between various chapters, comparing different notations and approaches, enabling readers from different backgrounds to benefit from it and to move around chapters at will. Worked examples and exercises are given, making the volume valuable for courses.Tutorial versions of SPECTRAMAP and SIRIUS are optionally available as a Software Supplement, at a low price, to accompany the text.
Data Handling in Science and Technology
Published: September 1992
...some excellent material... the tutorial questions and answers are generally helpful.
...can be recommended for almost every chemometrician. It seems particularly useful for learning methodological details and for preparing courses.
Chemometrics Intelligent Lab. Systems
...let us make use of this intellectual treasure!
...the book is a first class text in the underlying principles and practical use of pattern recognition techniques.
I highly recommend this book to readers interested in learning both the geometric and algebraic properties of multivariate pattern recognition.
- Introduction (R.G. Brereton). 1. Introduction to Multivariate Space (P.J. Lewi). 2. Multivariate Data Display (P.J. Lewi). 3. Vectors and Matrices: Basic Matrix Algebra (N. Bratchell). 4. The Mathematics of Pattern Recognition (N. Bratchell). 5. Data Reduction Using Principal Components Analysis (J.M. Deane). 6. Cluster Analysis (N. Bratchell). 7. SIMCA - Classification by Means of Disjoint Cross Validated Principal Components Models (O.M. Kvalheim, T.V. Karstang). 8. Hard Modelling in Supervised Pattern Recognition (D. Coomans, D.L. Massart). Software Appendices: SPECTRAMAP (P.J. Lewi). SIRIUS (O.M. Kvalheim, T.V. Karstang). Index.