Rapid multivariate curve resolution applied to near real-time process monitoring with HPLC/Raman data

This paper describes a novel approach to multivariate curve resolution (MCR) for rapid resolution of low-resolution chromatographic data from complex mixtures collected using a liquid-core Raman waveguide detector. Pure spectra with individual chromatographic profiles are extracted from HPLC-Raman chromatograms. In order to accurately and quickly extract the pure components, the data must be resolved by a series of smaller analyses and then recombined to form whole chromatograms. Discrete segments of individual resolved chemical and spectral features are combined based on correlation coefficients calculated across segments of the chromatogram. The method described here differs from traditional MCR by applying a series of small local models where the rank of each limited data segment can be more readily determined. The piecewise method also is much faster than a traditional MCR analysis of the full data set by requiring fewer iterations in an alternating least-squares optimization. The resulting estimated spectral profiles are highly correlated to pure analyte spectra making this an excellent method for rapid qualitative analysis for the identification of chemical species separated via low-resolution chromatography.