Chemometrics for Pattern Recognition - Hardcover

Brereton, Richard G.

Hardcover

ISBN 10: 0470987251 ISBN 13: 9780470987254

Publisher: Wiley, 2009

Synopsis

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.

About this title

About the Author

Professor Richard Brereton, is the Professor of Chemometrics at the University of Bristol, UK
He is head of the Centre for Chemometrics which carries out a variety of research work including forensic science, biological pattern recognition, pharmaceutical sciences, plastics analysis and how data captured from instrumentation should be treated. In 2006 he received the Theophilus Redwood Lectureship from the Royal Society of Chemistry. He has published extensively in the literature, including publishing two previous books with Wiley in 2003 and 2007.

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;

• 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.

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;

• 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.

Edition details

• Publisher: Wiley
• Publication date: 2009
• Language: English
• ISBN 10: 0470987251
• ISBN 13: 9780470987254
• Binding: Hardcover
• Edition number: 1
• Number of pages: 528