Non-negative Factor (NNF) Assisted Partial Least Square (PLS) Analysis of Excitation-Emission Matrix Fluorescence Spectroscopic Data Sets: Automating the Identification and Quantification of Multifluorophoric Mixtures.

Abstract

Excitation-emission matrix fluorescence spectroscopy is simple and sensitive techniques that generate the composite fluorescence fingerprints. EEMF can be used for the identification and quantification of the fluorophores without involving any pre-separation step provided a suitable data analysis approach is applied. In the present work, non-negative factor (NNF) assisted partial least square (PLS) analysis is used for the analysis of EEMF data sets acquired for the dilute aqueous mixtures of fluorophores. The proposed approach allows automatic selection of the optimum number of factors for NNF analysis by incorporating the Akaike information criterion. The proposed approach also incorporates the spectral correlation analysis for the automatic identification of the NNF retrieved EEMF spectral profiles. The NNF retrieved contribution values along with their real concentration values are subjected to PLS analysis to develop a calibration model. The proposed approach was successfully tested using EEMF data acquired for the dilute aqueous mixtures of Catechol, Hydroquinone, Indole, Tryptophan and Tyrosine. The results were evaluated using the various statistical parameters and each of them found to well within the expected limits. In summary, NNF assisted PLS analysis of EEMF technique allows automatized analysis of the multifluorophoric mixtures with minimum user inputs.