Abstract Multivariate Curve Resolution (MCR) covers a wide span of algorithms designed to
tackle the mixture analysis problem by expressing the original data through a bilinear model …

This article is a tutorial that focuses on the main aspects to be considered when applying
Multivariate Curve Resolution to analyze multicomponent systems, particularly when the …

Principal component analysis of a data matrix extracts the dominant patterns in the matrix in
terms of a complementary set of score and loading plots. It is the responsibility of the data …

In biological/bioenergetics research the response of a complex system to an externally
applied perturbation is often studied. Spectroscopic measurements at multiple wavelengths …

In the analytical environment, spectral data resulting from the analysis of samples often
represent mixtures. To extract Information about pure components often Is a major problem …

A new multivariate curve resolution method is presented and tested with data of various
levels of complexity. Rotational and intensity ambiguities and the effect of selectivity on …

Progress in the analysis of multicomponent processes and mixtures relies on the
combination of sophisticated instrumental techniques and suitable data analysis tools …

A method for extracting information from spec-troscopic data gathered during process
monitoring is described and applied to an industrial problem. The method allows the …

One of the major methods used to resolve spectral data by self-modeling techniques
requires the presence of pure variables. A pure variable is a variable that has an intensity …

Multivariate curve resolution methods can be used for the quantitation of the overlapping
components in a chromatographic peak. Initial qualitative solutions obtained by self …