Multivariate Pattern Recognition in Chemometrics

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.