Multivariate Pattern Recognition in Chemometrics

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.

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.