Enhanced MCR-ALS modeling of HPLC with fast scan fluorimetric detection second-order data for quantitation of metabolic disorder marker pteridines in urine.

This work presents the development of a liquid chromatographic method based on modeling entire fast scan fluorimetric detection second-order data with the multivariate curve resolution alternating least squares algorithm, for the simultaneous determination of five marker pteridines in urine samples. The modeling strategy involves the building of a single MCR-ALS model composed of matrices augmented in the spectral mode, i.e. time profiles remain invariant while spectra may change from sample to sample. This approach allowed us to separate and determine the whole analytes at once. The developed approach enabled us to determine five of the most important metabolic disorder marker pteridines: biopterin, neopterin, isoxanthopterin, pterin and xanthopterin, three of them presenting emission spectra with the same emission wavelength maxima. In addition, some of these analytes present overlapped time profiles. As a consequence of using the entire data sets, a considerable reduction of the data processing experimental time can be achieved. Results are compared with a previous strategy in which data were split in five different regions, and information about the figures of merit of the new strategy compared with the previously reported strategy is reported.