On second-order calibration based on multivariate curve resolution in the presence of highly overlapped profiles

• Published in: Analytica chimica acta Vol. 1096; pp. 53 - 60
• Main Authors: Alcaraz, Mirta R., Culzoni, María J., Ibañez, Gabriela A., Lozano, Valeria A., Olivieri, Alejandro C.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.02.2020
• Subjects: Multivariate curve resolution; Rotational ambiguity; Second-order calibration; Rotational ambiguity; Multivariate curve resolution; Second-order calibration
• ISSN: 0003-2670; 1873-4324
• DOI: 10.1016/j.aca.2019.10.053

Multivariate curve resolution has been applied to both simulated and experimental data sets where high or even complete overlapping occurs between component profiles in one data mode. It is shown that rotational ambiguity exists in the bilinear decomposition of the augmented data matrices built with second-order data for pure analyte standards and test samples containing uncalibrated interferents. However, even in the presence of rotational ambiguity, initialization based on the so-called purest variables in one of the data modes may allow one to develop analytical protocols with reasonable statistical indicators for the prediction of the analyte of interest. In one of the explored experimental systems, the analyte ciprofloxacin was determined in the presence of the interferent salicylate, measuring time decay-luminescence matrix data. The average prediction error was 0.02 mg L−1 in the test set, corresponding to a relative error of ca. 8%. In the second system, capillary electrophoresis with UV detection was employed to determine ciprofloxacin in aqueous samples in the presence of other fluoroquinolones, achieving analyte recoveries in the range 101–113%. Although further theoretical work may still be needed, the present analysis of the feasible component profiles after bilinear decomposition provides some clues to interpret the phenomenon. [Display omitted] •Multivariate curve resolution is applied to second-order data.•Strong or complete overlapping in one data mode is considered.•Rotational ambiguity is assessed.•Simulated and experimental data are studied.•Prediction results are analyzed depending on initialization and constraints.