Resolution of spectrally rank-deficient multivariate curve resolution: alternating least squares components in comprehensive two-dimensional liquid chromatographic analysis
The advancements in comprehensive two-dimensional liquid chromatographic systems have led to more efficient and effective analyses of complex biological samples. Correspondingly, there has been an increase in the data complexity. Several chemometric techniques have been proposed to extract the maximum amount of information and to overcome the numerous challenges associated with data analysis. However, quantification remains troublesome, especially when dealing with spectrally rank-deficient peaks. To this end, a new constraint for multivariate curve resolution-alternating least squares (MCR-ALS) is proposed for the separation of chromatographic peaks appearing within the same MCR-ALS component and dividing them among additionally created components with highly similar spectral characteristics. Application of the constraint to every sample individually leads to two important advantages: the need for the selection of an overall dividing point for all samples is avoided, and the serious issue of retention time shifting is handled by the algorithm. Manual quantification of the compounds of interest is facilitated because the peaks in the second-dimension chromatograms can be attributed to a single compound, but it leads to comparable percent relative standard deviations (% RSD) prior to and after application of the constraint. Nonetheless, the presence of a single compound in each component allows for automated quantification by simple summation of the second-dimension chromatograms. Copyright (C) 2012 John Wiley & Sons, Ltd.
multivariate curve resolution comprehensive two-dimensional liquid chromatography rank deficiency unimodality constraint spectral similarity