Abstract

‘White’ and ‘grey’ methods of data modeling have been employed to resolve the heterogeneous fluorescence from a fluorophore mixture of 9-cyanoanthracene (CNA), 10-chloro-9-cyanoanthracene (ClCNA) and 9,10-dicyanoanthracene (DCNA) into component individual fluorescence spectra. The three-component spectra of fluorescence quenching in methanol were recorded for increasing amounts of lithium bromide used as a quencher. The associated intensity decay profiles of differentially quenched fluorescence of single components were modeled on the basis of a linear Stern-Volmer plot. These profiles are necessary to initiate the fitting procedure in both ‘white’ and ‘grey’ modeling of the original data matrices. ‘White’ methods of data modeling, called also ‘hard’ methods, are based on chemical/physical laws expressed in terms of some well-known or generally accepted mathematical equations. The parameters of these models are not known and they are estimated by least squares curve fitting. ‘Grey’ approaches to data modeling, also known as hard-soft modeling techniques, make use of both hard-model and soft-model parts. In practice, the difference between ‘white’ and ‘grey’ methods lies in the way in which the ‘crude’ fluorescence intensity decays of the mixture components are estimated. In the former case they are given in a functional form while in the latter as digitized curves which, in general, can only be obtained by using dedicated techniques of factor analysis. In the paper, the initial values of the Stern-Volmer constants of pure components were evaluated by both ‘point-by-point’ and ‘matrix’ versions of the method making use of the concept of wavelength dependent intensity fractions as well as by the rank annihilation factor analysis applied to the data matrices of the difference fluorescence spectra constructed in two ways: from the spectra recorded for a few excitation lines at the same concentration of a fluorescence quencher or classically from a series of the spectra measured for one selected excitation line but for increasing concentration of the quencher. The results of multiple curve resolution obtained by all types of the applied methods have been scrutinized and compared. In addition, the effect of inadequacy of sample preparation and increasing instrumental noise on the shape of the resolved spectral profiles has been studied on several datasets mimicking the measured data matrices.Graphical ᅟ

Highlights

  • The rapidly developing methods of chemical analysis are nowadays those involving self-modeling curve resolution (SMCR) of a spectral data matrix representing aJ Fluoresc (2018) 28:615–632Upon the use of a proper transformation matrix the abstract matrices could be converted into the predicted profiles of both types of variability [2].For the first time, the concept of SMCR was successfully elaborated and applied in the early 1970s by Lawton and Sylvestre [3]

  • The analysis of the obtained results reveals that the method of ‘indirect’ rank annihilation factor analysis, τ-RAFA, can be successfully employed to determine the number of components in the multi-component system of quenched fluorescence and to estimate their Stern-Volmer quenching constants

  • The κ-RAFA algorithm should only be used as a tool in pre-analysis of the collected data or to confirm findings already unveiled by other methods - the Authors suggest using both RAFA approaches concomitantly, after initial determination of the number of principal components

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Summary

Introduction

The rapidly developing methods of chemical analysis are nowadays those involving self-modeling curve resolution (SMCR) of a spectral data matrix representing aJ Fluoresc (2018) 28:615–632Upon the use of a proper transformation matrix the abstract matrices could be converted into the predicted profiles of both types of variability [2].For the first time, the concept of SMCR was successfully elaborated and applied in the early 1970s by Lawton and Sylvestre [3]. By keeping the same minimum set of constraints and imposing a constant value on all three elements of one vector of the transformation matrix, the three-dimensional problem was reduced to two dimensions This allowed to determine an appropriate set of the elements of the remaining two other vectors of the transformation matrix and to visualize the area of feasible solutions (AFS) for the pure component spectra. The selection of this so called T-space representation of the three-component data was carried out by the Monte Carlo method producing feasible spectral bands for all components of the three-component system [4]. The so called Borgen plots, preserving the two intrinsic assumptions of soft data modeling, were successively modified by adding some other constraints narrowing the bands of the AFS computed spectra and concentration profiles [7,8,9,10,11]

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