Application of radial basis function neural network and DFT quantum mechanical calculations for the prediction of the activity of 2-biarylethylimidazole derivatives as bombesin receptor subtype-3 (BRS-3) agonists

Application of unfolded principal component analysis-radial basis function neural network for determination of celecoxib in human serum by three-dimensional excitation-emission matrix fluorescence spectroscopy

This study describes a simple and rapid approach of monitoring celecoxib (CLX). Unfolded principal component analysis-radial basis function neural network (UPCA-RBFNN) and excitation-emission spectra were combined to develop new model in the determination of CLX in human serum samples. Fluorescence landscapes with excitation wavelengths from 250 to 310nm and emission wavelengths in the range 280-450nm 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 CLX and a recovery of 103.63% was obtained. The results indicated that the proposed method is an interesting alternative to the traditional techniques normally used for determining CLX such as HPLC.