1521-4109/asset/2049_left.gif?v=1&s=d971976ddd1fb423bc0ed04ac08d79fc2a6500de)
Review
Multivariate Data Analysis in Electroanalytical Chemistry
Article first published online: 28 NOV 2002
DOI: 10.1002/1521-4109(200211)14:22<1533::AID-ELAN1533>3.0.CO;2-T
© 2002 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
1521-4109/asset/cover.gif?v=1&s=209b447dcf920ba3fb44f2b08a87a6b586320422 "Electroanalysis")
Additional Information
How to Cite
Richards, E., Bessant, C. and Saini, S. (2002), Multivariate Data Analysis in Electroanalytical Chemistry. Electroanalysis, 14: 1533–1542. doi: 10.1002/1521-4109(200211)14:22<1533::AID-ELAN1533>3.0.CO;2-T
Publication History
- Issue published online: 28 NOV 2002
- Article first published online: 28 NOV 2002
- Manuscript Accepted: 17 MAY 2002
- Manuscript Received: 8 APR 2002
- Abstract
- References
- Cited By
Keywords:
- Multivariate data analysis;
- Calibration;
- Classification;
- Principal components;
- Neural networks;
- Genetic algorithms
Abstract
Data analysis is becoming an increasingly important aspect of electroanalytical chemistry, as voltammetric techniques and electrode arrays become ever more popular as diagnostic tools. Modern data analysis techniques promise to help us make full use of the large amounts of data collected, allowing electroanalytical chemists to get more out of their existing instruments, and paving the way for new measurement approaches. This article provides an overview of the most widely used multivariate techniques in electroanalysis, citing specific examples of how they have been applied, and looking at their relative merits. As in other areas of analytical science, no single technique is applicable to all applications and the running of controls and appreciation of the applications and limitations of each technique is essential.