Steady state kinetic model constraint for Multivariate Curve Resolution-Alternating Least Squares analysis

The interpretation of principal component analysis (PCA) models of complex biological or chemical data can be cumbersome because in PCA the decomposition is performed without any knowledge of the system at hand. Prior information of the system is not used to improve the interpretation. In this paper we introduce Grey Component Analysis (GCA) as a new explorative data analysis method that uses the available prior information. GCA uses a soft penalty approach to gently push the decomposition into the direction of the prior information. The grey components are therefore partly data driven and partly driven by the prior information. GCA works in a confirmatory mode to analyze the validity of the prior information and in an exploratory mode in which new phenomena can be studied in detail. To show the wide applicability of GCA, applications within spectroscopy and gene expression are presented. Many diagnostic properties of GCA are introduced and examples of erroneous parts within the prior information are indicated.

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Grey component analysis

Westerhuis

Derks

Hoefsloot

et al. 2007

Journal of Chemometrics

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A mixed hard- and soft-modelling approach to study and monitor enzymatic systems in biological fluids

Amigo

Juan

Coello

et al. 2006

Analytica Chimica Acta

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Estimating the rate constant in second-order kinetics using hard-soft-net analyte signal (HS-NAS) method

Hemmateenejad

Absalan

Nekoeinia

et al. 2010

Chemometrics and Intelligent Laboratory Systems

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Steady state kinetic model constraint for Multivariate Curve Resolution-Alternating Least Squares analysis

Cited by 9 publications

References 43 publications

Grey component analysis

Grey component analysis

A mixed hard- and soft-modelling approach to study and monitor enzymatic systems in biological fluids

Estimating the rate constant in second-order kinetics using hard-soft-net analyte signal (HS-NAS) method

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