Modeling Nonbilinear Total Synchronous Fluorescence Data Matrices With A Novel Adapted Partial Least Squares Method

Keywords

Ciprofloxacin; Residual modeling; Second-order advantage; Synchronous fluorescence

Abstract

A new residual modeling algorithm for nonbilinear data is presented, namely unfolded partial least squares with interference modeling of non bilinear data by multivariate curve resolution by alternating least squares (U-PLS/IMNB/MCR-ALS). Nonbilinearity represents a challenging data structure problem to achieve analyte quantitation from second-order data in the presence of uncalibrated components. Total synchronous fluorescence spectroscopy (TSFS) generates matrices which constitute a typical example of this kind of data. Although the nonbilinear profile of the interferent can be achieved by modeling TSFS data with unfolded partial least squares with residual bilinearization (U-PLS/RBL), an extremely large number of RBL factors has to be considered. Simulated data show that the new model can conveniently handle the studied analytical problem with better performance than PARAFAC, U-PLS/RBL and MCR-ALS, the latter modeling the unfolded data. Besides, one example involving TSFS real matrices illustrates the ability of the new method to handle experimental data, which consists in the determination of ciprofloxacin in the presence of norfloxacin as interferent in water samples.

Publication Date

2-15-2015

Publication Title

Analytica Chimica Acta

Volume

859

Number of Pages

20-28

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.aca.2014.12.014

Socpus ID

84921613740 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84921613740

This document is currently not available here.

Share

COinS