Interpretation of matrix chromatographic-spectral data modeling with parallel factor analysis 2 and multivariate curve resolution.

Parallel factor analysis 2 (PARAFAC2) is still being advocated for the processing of second-order chromatographic-spectral data, both for qualitative and quantitative applications. However, neither classical PARAFAC2 nor the newly developed flexible non-negative NN-PARAFAC2 version can adequately model these data in a general situation. In quantitative analysis, considerable bias may result in the estimation of analyte concentrations, due to the fact that both PARAFAC2 models apply an artificial constraint to the retrieved profiles, requiring constant cross-product, i.e., constant overlapping, between all pairs of component elution profiles in all samples. This only occurs under limited conditions. In this report, simulations help to understand, visualize and interpret these PARAFAC2 features. Experimental data are also studied concerning the determination of a fluoroquinolone antibiotic in bovine liver samples by liquid chromatography with multi-wavelength fluorescence detection. Both for simulated and experimental data, the PARAFAC2 versions provide poor analytical results, while correct data processing and reasonable analytical indicators can be achieved using multivariate curve resolution - alternating least-squares (MCR-ALS). For the simulated data sets, root mean square errors/relative errors of prediction were 0.01 concentration units/2% for MCR-ALS, compared to 0.02-0.06 units/4-12% for both PARAFAC2 and NN-PARAFAC2. For the experimental data sets, they were 0.025 µg mL/11% for MCR-ALS, 0.09 µg mL/40% for PARAFAC2 and 0.16 µg mL/71% for NN-PARAFAC2, with average recoveries (standard deviation) of 91(14)%, 185(135)% and 69(35)% respectively.