Caracterização de açúcar mascavo aplicando análise das componentes principais a dados espectrométricos

• Autor(a) principal: Natalino, Ricardo
• Data de Publicação: 2006
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: LOCUS Repositório Institucional da UFV
• Texto Completo: http://locus.ufv.br/handle/123456789/2055
• topic: Química analítica; Análise de componentes principais; Espectrometria de infravermelho por reflectância; Cromatografia a líquido de alta eficiência; Açúcar mascavo; Análise multivariada; Analytical chemistry; Analysis of the main components; Infra-red reflectance spectrometry; Liquid chromatography of high efficiency; Brown sugar; Multivariate analysis; CNPQ::CIENCIAS EXATAS E DA TERRA::QUIMICA::QUIMICA ANALITICA

The purpose of this work is the characterization of the samples of brown sugar, clear (DPC) and dark (DPE), from the region of Cataguases, for the Puxa-Puxa Candy Industry, determining the main associations of these, as well as expressing the indices of correlation between the same ones, based in the data of specters in the next infra-red ray, measures analytical and of chemiometric methods using analysis of the main components, that is one of the many techniques of analysis. The diffuse reflectance specters in the region of the next infra-red ray had been using a spectrophotometer NIR System 6500 (Silver Spring, MD, the USA) using cell coarse and region of the 2500 specter of 1000 nm, with increment of 2nm. For the determination of the concentrations of metals (Cu, Ca, In, Fe, Mg) atomic absorption of the VARIAN spectrophotometer, SpectrAA-200 model was used. For the determination of the text of sacarose it used a technique of Liquid Chromatography of High Efficiency with a detector of Refractive index of the Shimadzu mark, model RID - 10A. The data has been transferred to the environment Matlab 5.3. In this environment, the specters will be daily pay-processed centering in the average. After that the method of the Main Component Analysis (PCA) will be used to certify the separation of the samples of brown sugar. The main components analysis turned evident the common and discrepant characteristics between the different brown sugar. In the disposal of the data of the specters in the PCA it is possible to observe that the first main component (PC1) explains 93,60 % of the total variance of the data, it was observed that the separation exists clearly entering the group of brown sugar (DPC) and the group of the dark brown sugar (DPE), in the first main component. The PCA to the matrix of data generated (97 x 5) with the results of the concentrations of metals had been applied. The daily pay - used processing was the auto-scheduling, where it showed respectively that the first main component (PC1) explains 56,24 % of the total variance of the data, being that the second main component (PC2) explains 27,27 %. It is interesting to notice that the different samples have been grouped between itself, in accordance with the differences in the concentrations of joined metals.