Multivariate spectrofluorimetric detection of lipase isolated from Serratia marcescens in chromatographic fractions

Lipases are ubiquitous enzymes and widespread in nature. They have been widely purified as one of the most important enzymes in molecular biosciences and biotechnology. In this paper, the extracellular lipase was separated from Serratia marcescens. The separated enzyme was purified partially by ammonium sulfate precipitation, dialysis and gel filtration chromatography. Presence of the lipase in chromatography fractions was assayed by the hydrolysis of paranitrophenyl palmitate (pNPP) as substrate. The excitation and emission (EEM) fluorescence spectra of purified lipase in chromatographic fractions were investigated. The study demonstrates an application of fluorescence spectroscopy, combined with multivariate regression methods, to the analysis of fluorescent lipase component. N-way partial least squares (N-PLS) was utilized to show the importance of region selection in calibration modeling of the data. Genetic algorithm (GA) optimization was applied to improve the performance of radial basis function network based regression model. RBF-ANN was used to calibrate and predict lipase activity. The analytical performance of RBF-ANN method was characterized by Q^2parameter. The value of Q^2 was 0.919. The proposed method was successfully applied to the analysis of protein containing fractions and in order to explore the three way fluorescence data array from separated fractions, parallel factor analysis (PARAFAC) was applied. The fluorescence signal was resolved into excitation and emission profiles of the pure fluorescent compounds.