Chemometrics: Difference between revisions
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==Applications== | ==Applications== | ||
In [[spectroscopy]], the applications of chemometrics is most often in calibration. Calibration is achieved by using the spectra as multivariate descriptors to predict concentrations of constituents of interest using statistical approaches such as [[Linear regression|Multiple Linear Regression]], [[Principal components analysis]] and | In [[spectroscopy]], the applications of chemometrics is most often in calibration. Calibration is achieved by using the spectra as multivariate descriptors to predict concentrations of constituents of interest using statistical approaches such as [[Linear regression|Multiple Linear Regression]], [[Principal components analysis]] and Partial Least Squares. Other popular chemometry techniques include approaches for ''ab initio'' prediction of number of components, noise reduction and multivariate curve resolution. | ||
==See also== | ==See also== | ||
Revision as of 15:12, 9 January 2009
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Chemometrics is the application of mathematical or statistical methods to chemical data. The International Chemometrics Society (ICS) offers the following definition:
Chemometric research spans a wide area of different methods which can be applied in chemistry. There are techniques for collecting good data (optimization of experimental parameters, design of experiments, calibration, signal processing) and for getting information from these data (statistics, pattern recognition, modeling, structure-property-relationship estimations).
Chemometrics tries to build a bridge between the methods and their application in chemistry.
Applications
In spectroscopy, the applications of chemometrics is most often in calibration. Calibration is achieved by using the spectra as multivariate descriptors to predict concentrations of constituents of interest using statistical approaches such as Multiple Linear Regression, Principal components analysis and Partial Least Squares. Other popular chemometry techniques include approaches for ab initio prediction of number of components, noise reduction and multivariate curve resolution.
See also
References
- R. Kramer, Chemometric Techniques for Quantitative Analysis, (1998) Marcel-Dekker, ISBN: 0-8247-0198-4.
- D.L. Massart, B. Vandeginste, L. Buydens, S. De Jong, P. Lewi and J. Smeyers-Verbeke, Handbook of Chemometrics and Qualimetrics, Vol 20 A, (1997), Elsevier, ISBN: 0-444-89724-0; Vol 20 B, (1998), Elsevier, ISBN: 0-444-82853-2
- R. G. Brereton, Chemometrics: Data Analysis for the Laboratory and Chemical Plant, (2003) Wiley, ISBN: 978-0-471-48978-8.
External links
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