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Master's Dissertation
DOI
https://doi.org/10.11606/D.76.1997.tde-09032009-150936
Document
Author
Full name
Ana Carolina de Mattos Zeri
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 1997
Supervisor
Committee
Bonagamba, Tito Jose (President)
Biscegli, Clovis Isberto
Terrile, Maria Cristina
Title in Portuguese
Aplicação de Análise Multivariada de dados a Espectroscopia de Alta Resolução em Sólidos por Ressonância Magnética Nuclear.
Keywords in Portuguese
Análise multivariada
Ressonância magnética nuclear
Sólidos
Abstract in Portuguese
Neste trabalho utilizamos as técnicas de espectroscopia de alta resolução em sólidos por Ressonância Magnética Nuclear de 13C, Polarização Cruzada, Desacoplamento Heteronuclear e Rotação da Amostra em tomo do Angulo Mágico para o estudo de sementes e alimentos. Uma técnica de analise multivariada foi introduzida com o intuito de se desenvolver um método de calibração dos espectros a partir dos experimentos com amostras padrão, de maneira que esse método permita a determinação das concentrações dos componentes da amostra através de uma multiplicação matricial. Essa técnica consiste basicamente da Decomposição em Valores Singulares de uma matriz composta pelos espectros, seguida da regressão linear múltipla visando encontrar uma matriz de regressão entre a matriz de espectros e a matriz de concentrações das principais componentes das amostras. Essa matriz de regressão, multiplicada pelo espectro de uma nova amostra permite a previsão das concentrações dos componentes desta. As concentrações de proteína e amido foram avaliadas para cereais e alguns alimentos industrializados.
Title in English
Multivariate data analysis applied to high resolution spectroscopy in solids by nuclear magnetic resonance.
Keywords in English
Multivariate analysis
Nuclear magnetic resonance
Solids
Abstract in English
High Resolution Solid-State 13C-NMR Spectroscopy techniques Cross Polarization, Decoupling and Magic Angle Spinning, were employed in this work for the study of the chemical composition of seeds and food. A Multivariate Analysis procedure was also employed in the development of a calibration and prediction method for the determination of the components content based on a matrix multiplication. Singular Value Decomposition was carried on the 13C-NMRspectra matrix followed by Multiple Linear Regression on the components content matrix with the purpose of producing a model that relates the spectra to the sample components content determined by referee methods. When the resulting model is then applied to a new sample, assuming that the correlation found between the calibrations set matrices also exists in this sample, it gives the components content values. The protein and starch content were analyzed.
 
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Publishing Date
2009-03-19
 
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