Competitive adaptive reweighted sampling assisted partial least square analysis of excitation-emission matrix fluorescence spectroscopic data sets of certain polycyclic aromatic hydrocarbons.

Excitation-emission matrix fluorescence (EEMF) spectroscopy is a simple and sensitive analytical technique. EEMF spectrum is essentially a collection of emission and excitation spectra acquired as increasing functions of excitation and emission wavelengths, respectively. EEMF spectral data sets produced per sample are highly correlated and larger in amount that need the assistance of chemometric techniques such partial least square (PLS) analysis if one desire to build robust calibration model. The objective of the PLS algorithm is to explain maximum variation of the spectral and concentration data matrices and to maximise the correlation between them. The application of a suitable variable selection technique can significantly improve the performance of PLS calibration model. Towards this, the present work proposes application of competitive adaptive reweighted sampling (CARS) as a variable selection approach prior to PLS analysis of EEMF spectral data sets. The utility of proposed approach was successfully demonstrated by analysing the significantly overlapped EEMF spectral data set of aqueous mixtures of Anthracene, Chrysene, Fluoranthene and Pyrene that are highly carcinogenic and mutagenic in nature. The developed procedure was also successfully used for the analysis of Chrysene and Pyrene mixtures in gasoline spiked ground water samples. The CARS assisted PLS model was also compared with full spectrum PLS, genetic algorithm assisted PLS, ant colony optimisation assisted PLS and N-way PLS models. The obtained results of the present work clearly indicated that application of PLS algorithm on CARS optimised EEMF spectral variables significantly improved the performance of the calibration models.