Chemometrics for Pattern Recognition (PDF)

Over the past decade, pattern recognition has been one of the
fastest growth points in chemometrics. This has been catalysed by
the increase in capabilities of automated instruments such as LCMS,
GCMS, and NMR, to name a few, to obtain large quantities of data,
and, in parallel, the significant growth in applications especially
in biomedical analytical chemical measurements of extracts from
humans and animals, together with the increased capabilities of
desktop computing. The interpretation of such multivariate datasets
has required the application and development of new chemometric
techniques such as pattern recognition, the focus of this work.

Included within the text are:

* 'Real world' pattern recognition case studies from
a wide variety of sources including biology, medicine, materials,
pharmaceuticals, food, forensics and environmental science;

* Discussions of methods, many of which are also common in
biology, biological analytical chemistry and machine learning;

* Common tools such as Partial Least Squares and Principal
Components Analysis, as well as those that are rarely used in
chemometrics such as Self Organising Maps and Support Vector
Machines;

* Representation in full colour;

* Validation of models and hypothesis testing, and the underlying
motivation of the methods, including how to avoid some common
pitfalls.

Relevant to active chemometricians and analytical scientists in
industry, academia and government establishments as well as those
involved in applying statistics and computational pattern
recognition.