A systematic study on the effect of noise and shift on multivariate figures of merit of second-order calibration algorithms.

In the present study, multivariate analytical figures of merit (AFOM) for three well-known second-order calibration algorithms, parallel factor analysis (PARAFAC), PARAFAC2 and multivariate curve resolution-alternating least squares (MCR-ALS), were investigated in simulated hyphenated chromatographic systems including different artifacts (e.g., noise and peak shifts). Different two- and three-component systems with interferences were simulated. Resolved profiles from the target components were used to build calibration curves and to calculate the multivariate AFOMs, sensitivity (SEN), analytical sensitivity (γ), selectivity (SEL) and limit of detection (LOD). The obtained AFOMs for different simulated data sets using different algorithms were used to compare the performance of the algorithms and their calibration ability. Furthermore, phenanthrene and anthracene were analyzed by GC-MS in a mixture of polycyclic aromatic hydrocarbons (PAHs) to confirm the applicability of multivariate AFOMs in real samples. It is concluded that the MCR-ALS method provided the best resolution performance among the tested methods and that more reliable AFOMs were obtained with this method for the studied chromatographic systems with various levels of noise, elution time shifts and presence of unknown interferences.