Aplicações de métodos quimiométricos e de técnicas analíticas em amostras alimentícias e farmacêuticas

Abstract

This paper comprises two different researches: the firts one was related to a medicament for which was developed a methodology to quantify Norfloxacin (NOR) by molecular spectrofluorimetry and validation by HPLC. First of all, was developed a methodology for spectrofluorimetry where were done some preliminary tests to establish which value of pH would provide the highest emission intensity. After fix pH, it was performed NOR determination in aqueous standard and medicament using univariate calibration. The concentration range was 0-500 μg.L-1. The limit of detection for medicament was 6,9 μg.L-1 while the limit of detection was 24,6 μg.L-1. Beyond that, other figures of merit were also estimated for developing methodology and offered results quite satisfactory, such as recovering tests in which analyte recovery was 99.5 - 103.8%. For identify and quantify NOR in urine was necessary to dilute the samples (studied in two different levels: 500 and 1000 X) and also standar method addition (in same concentration range used for medicament). After spectra acquisition, all of them were used to built the tensor that would be used in PARAFAC. It was possible to estimate figures of merit such as limit of detection: 11.4 μg.L-1 and 8.4 μg.L-1 (dilution of 500 and 1000 X respectively) and limit of quantification: 34 μg.L-1 and 25.6 μg.L-1 (dilution of 500 and 1000 X, respectively). The second studied case was in food area in which was used NIR and FT-MIR spectroscopy coupled with chemometric to discriminate transgenic and no transgenic soybean oil. Oils spectra did not show significative difference in visual terms, being necessary using chemometric tools capable of perform this distinction. For NIR and FT-MIR spectroscopy it was performed PCA that aimed identify anomalous samples that would be a negative influence. After perform PCA were used three different techniques to discriminate oils samples: SIMCA, SVM-DA and PLS-DA, being used for each technique different preprocessing. In NIR, only for one preprocessing it was obtained good results for three techniques while in FT-MIR when PLS-DA was used it was achieved 100% in classification for all preprocessing