Introducing 'Simple Variable Selection (SVS) Approach' for Improving the Quantitative Accuracy of Chemometric Assisted Fluorimetric Estimations of Dilute Aqueous Mixtures.

Excitation emission matrix fluorescence (EEMF) spectroscopy is a multiparametric fluorescence technique where the fluorescence intensity of a fluorophore is a function of excitation wavelength, emission wavelength and its concentration. The manual analysis of large volume of highly correlated EEMF data sets towards developing a calibration model for quantifying each fluorophores present in multifluorophoric mixtures is a difficult and time-consuming task. Over the years, Partial least square (PLS) algorithm has found its application towards providing swift and efficient analyses of large volumes of highly correlated spectral data sets. The PLS assisted EEMF spectroscopy has been successfully used towards quantifying the fluorophores in multifluorophoric mixtures without involving any pre-separation. However, the accuracy and robustness of developed calibration model can be significantly improved provided PLS analysis is carried out on the analytically relevant EEMF spectral variables. In the present work, a variable selection method baptized as simple variable selection (SVS) approach is introduced that provides a simple and computationally economical means of identifying the useful spectral variables for subsequent PLS analysis. The proposed SVS approach is successfully validated by analyzing the complex EEMF data sets of multifluorophoric mixtures of consisting of multifluorophoric mixtures of biological relevance. The proposed approach is found to provide a simple, swift and efficient means for developing a robust PLS assisted EEMF spectroscopy based calibration model for simultaneous quantification of various fluorophores present in multifluorophoric mixtures.