When unfolding is better: unique success of unfolded partial least-squares regression with residual bilinearization for the processing of spectral–pH data with strong spectral overlapping. Analysis of fluoroquinolones in human urine based on flow-injection pH-modulated synchronous fluorescence data matrices

Abstract

Synchronous fluorescence spectra measured in a flow-injection system with double pH gradient modulation constitute a new second-order signal which is herein studied for the quantitative determination of three fluoroquinolone antibiotics in spiked human urine samples. Because calibration is done using aqueous solutions of each of the three analytes ciprofloxacin, norfloxacin and ofloxacin, the fluorescent urine background makes it necessary to achieve the second-order advantage. Several second-order multivariate calibration algorithms were evaluated for this purpose: parallel factor analysis, unfolded and multiway partial least-squares with residual bilinearization, and multivariate curve resolution-alternating least-squares. The best analytical figures of merit, a root mean square error of 4-6 mg L(-1) (corresponding to a relative error of 4-6% for a calibration range from 0 to 200 mg L(-1) for each analyte), and a limit of detection of 4 mg L(-1) were obtained using partial least-squares (in the specific unfolded version) combined with residual bilinearization. Reasons for the improved success of this latter technique are provided on the basis of the analysis of simulated second-order data.