Processing Excitation-Emission Matrix Fluorescence and Total Synchronous Fluorescence Spectroscopy Data Sets with...

The present work successfully shows the application of novel chemometric approach constraint randomised non-negative factor analysis (CRNNFA) for the analyses of the composite multidimensional fluorescence data sets. The CRNNFA involves the initialisation of the spectral variables in a constraint fashion thus ensures that algorithm does not wander with chemically and spectro-chemically irrelevant variables. The CRNNFA approach does not require that there must be pure variables for each fluorophores of the multifluorophoric mixture. One of the biggest advantages of CRNNFA is that it does not involve any convergence criteria thus circumventing the premature convergence of the algorithm. The CRNNFA achieves the termination only when the iteration limit is reached. The CRNNFA analysis s carried out under the non-negativity constraints therefore the mathematically retrieved profiles can easily be compared with those obtained experimentally. In the present work, both trilinear as well as non-trilinear multidimensional data sets are subjected to CRNNFA to validate its applicability. Excitation emission matrix fluorescence (EEMF) spectral profiles of Catechol, Hydroquinone, Indole and Tryptophan mixtures is used as the source of trilinear data sets. Total synchronous fluorescence spectroscopy (TSFS) spectral profiles of Benzo[a] Pyrene, Chrysene and Pyrene mixtures are used as the source of non-trilinear data sets. The CRNNFA approach is found to work equally well with trilinear as well with non-trilinear data sets. Thus, CRNFFA clearly does not have any prerequisite in the data structure. The obtained results clearly shows that CRNNFA algorithm in combination with EEMF and TSFS data sets are potential analytical tool for the analysis of complex-multifluorophoric mixtures.