Avaliação de porfirinas na determinação simultânea de cátions por espectofotometria uv-vis e calibração multivariada

• Autor(a) principal: Nino, Ivson de Carvalho
• Data de Publicação: 2014
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações da UFPB
• Texto Completo: https://repositorio.ufpb.br/jspui/handle/tede/7160
• topic: Determinação simultânea; Metais; Mesoporfirinas; Calibração multivariada; Sseleção de variáveis; Simultaneous determination; Metals; Mesoporphyrins; Multivariate calibration; Variables selection; CIENCIAS EXATAS E DA TERRA::QUIMICA

In this work, we have investigated the use of porphyrins as non-selective complexing agents for the simultaneous determination of cations Pb2+, Zn2+, Cu2+, Mn2+, Co2+, and Hg2+ employing first-order multivariate calibration. At first, given its widespread availability and lower cost, the 5,10,15,20-tetraphenylporphyrin (H2TPP) was used in organic medium to complex cations in aqueous medium. Due to the instability of this system, the completion of this proposal was not achieved, but some important aspects are presented. Alternatively, we evaluated the use of 5,10,15,20-tetrakis (4-carboxyphenyl) porphyrin (H2TCPP) in aqueous medium. A 24-1 fractional factorial design indicated that the best conditions of metallation were pH 9, with the reaction performed in 10 minutes, at a temperature of 80°C. The best concentration of catalyst (Cd2+) was 5 x 10-8 mol L-1. A calibration set was constructed employing a Brereton design for six cations at five concentration levels. External validation was used with a set of ten samples containing random concentrations of analytes. Calibration models were constructed based on partial least-squares regression (PLS) and multiple linear regression (MLR) combined with variable selection by genetic algorithm (GA), or the successive projections algorithm (SPA). The method was employed in the analysis of mineral water samples and good apparent recoveries were obtained when spiked samples were predicted by SPA-MLR model.