The effect of data matrix augmentation and constraints in extended multivariate curve resolution-alternating least squares

The reliability of results obtained by multivariate curve resolution (MCR) methods is strongly dependent on the absence or presence of a small degree of rotational ambiguity associated to them. In this work, the effect of rotational ambiguities on the profiles resolved by MCR methods is examined in detail for cases of interest to analytical chemistry, where a number of calibration samples are usually prepared containing analyte standards, while test samples may contain additional uncalibrated constituents. These multiple chemical data sets having common constituents are simultaneously analyzed using matrix augmentation strategies. In these cases, conditions for better resolution and improved profiles are more easily achieved. To evaluate the extension of rotational ambiguities and to quantify their reduction after matrix augmentation, we applied the MCR-BANDS procedure. Results obtained by the application of this procedure confirmed that the simultaneous analysis of multiple data sets decreased considerably the extension of rotational ambiguities compared with those obtained when only a single data set is analyzed. Simulated and experimental data sets of interest to second-order analytical calibration are discussed. The effect of rotational ambiguities on the profiles resolved by multivariate curve resolution (MCR) is examined for multiple chemical data sets having common constituents are simultaneously analyzed using matrix augmentation. Conditions for better resolution and improved profiles are more easily achieved. To evaluate the rotational ambiguities and to quantify their reduction after augmentation, we applied MCR-BANDS. The obtained results confirmed that the simultaneous analysis of multiple data sets decreased considerably the rotational ambiguities compared with single matrix analysis.