A validated spectrofluorometric method for the determination of celecoxib in capsules

A rapid, selective, sensitive and simple fluorescence method was developed for the direct determination of celecoxib in capsules. The capsules were emptied, pulverized and dissolved in either ethanol or acetonitrile, sonicated and filtered. Direct fluorescence emission was measured at 355±5 nm (exciting at 272 nm). The method was fully validated and the recoveries were excellent, even in presence of excipients.     

A theoretical investigation of the preferred conformations of glutathione and its constituent amino acid residues

The preferred conformations of the tripeptide glutathione have been investigated by performing quantum mechanical calculations using the PCILO method. A series of model compounds representing fragments of the tripeptide has been studied as well as the complete molecule. The results are compared with the available experimental data.     

Synthesis of novel proline-thiazole based cyclic hexa- and octapeptides

Novel proline-thiazole based cyclopeptides were produced by cyclooligomerisation of an L-proline thiazole amino acid HCl in the presence of pentafluorophenyl diphenylphosphinate (FDPP) or diphenyl phosphorazidate (DPPA).     

Hydride generation atomic fluorescence spectrometric determination of ultratraces of selenium and tellurium in cow milk

A sensitive procedure has been developed for selenium and tellurium determination in milk by hydride generation atomic fluorescence spectrometry (HG-AFS) after microwave-assisted sample digestion. The method provides sensitivity values of 1591 and 997 fluorescence units ng⁻¹ ml⁻¹ with detection limits of 0.005 and 0.015 ng ml⁻¹ for Se and Te, respectively. The application of the developed methodology to the analysis of cow milk samples of the Spanish market evidenced the presence of concentration ranges from 11.1 to 26.0 ng ml⁻¹ for Se, and from 1.04 to 9.7 ng ml⁻¹ for Te having found a good comparability with data obtained after dry-ashing of samples.     

Simulation of aerated lagoon using artificial neural networks and multivariate regression techniques

The aim of this study was to develop an empirical model that provides accurate predictions of the biochemical oxygen demand of the output stream from the aerated lagoon at International Paper of Brazil, one of the major pulp and paper plants in Brazil. Predictive models were calculated from functional link neural networks (FLNNs), multiple linear regression, principal components regression, and partial least-squares regression (PLSR). Improvement in FLNN modeling capability was observed when the data were preprocessed using the PLSR technique. PLSR also proved to be a powerful linear regression technique for this problem, which presents operational data limitations.