Electrochemical sensor highly selective for estradiol valerate determination based on a modified carbon paste with iron tetrapyridinoporphyrazine.

This work reports the use of iron tetrapyridinoporphyrazine (FeTPyPz) as a highly selective catalyst in the construction of an electrochemical sensor for estradiol valerate (EV) determination. The sensor was prepared by modifying a carbon paste with FeTPyPz. The best results were obtained in a mixture of acetonitrile (MeCN) and 0.1 mol L(-1) phosphate buffer solution (pH 6.0) in a volume ratio of 47 : 53. A linear response range was observed between 45 and 450 micromol L(-1) with a sensitivity of 12160 +/- 306 microA L mol(-1) and quantification and detection limits of 45 and 13 micromol L(-1), respectively. The repeatability, expressed as the relative standard deviation (RSD) for n = 10, was 5.9% ([EV] = 50 micromol L(-1)). The reproducibility (RSD) for the sensor construction was better than 4% and the operational stability (RSD) over 50 measurements was 1.8%. A detailed investigation regarding the selectivity and electrochemical characteristics was carried out. Finally, in a first step to evaluate the application potential of the sensor, it was successfully applied to determine EV in a commercial formulation.

Solid-phase spectrofluorimetric determination of acetylsalicylic acid and caffeine in pharmaceutical preparations using partial least-squares multivariate calibration.

PLS-1, a variant of the partial least-squares algorithm was used for the solid-phase spectrofluorimetric determination of acetylsalicylic acid (ASA) and caffeine (CF) in pharmaceutical formulations. The method allows the simultaneous quantification of the analytes, as the closely overlapping spectral bands are efficiently solved. Sample preparation prior to analysis is not required. The calibration set consisted of 83 samples with 50-170mgg(-1) ASA plus 5-20mgg(-1) CF; another set of 25 samples was used for external validation. Agreement between predicted and experimental concentrations was fair (r=0.987 and 0.974 for ASA and CF models). For both models, the prediction performance was evaluated in terms of the coefficient of variability (CV), relative predictive determination (RPD), and ratio error range (RER). The final PLS-1 models were used for the determination of ASA and CF in pharmaceutical formulations.