Combined Unfolded Principal Component Analysis and Artificial Neural Network for Determination of Ibuprofen in Human Serum by Three-Dimensional Excitation–Emission Matrix Fluorescence Spectroscopy

Abstract

This study describes a simple and rapid approach of monitoring ibuprofen (IBP). Unfolded principal component analysis-artificial neural network (UPCA-ANN) and excitation-emission spectra resulted from spectrofluorimetry method were combined to develop new model in the determination of IBF in human serum samples. Fluorescence landscapes with excitation wavelengths from 235 to 265 nm and emission wavelengths in the range 300–500 nm were obtained. The figures of merit for the developed model were evaluated. High performance liquid chromatography (HPLC) technique was also used as a standard method. Accuracy of the method was investigated by analysis of the serum samples spiked with various concentration of IBF and an average relative error of prediction of 0.18% was obtained. The results indicated that the proposed method is an interesting alternative to the traditional techniques normally used for determination of IBF such as HPLC.