Orientador: Ronei Jesus Popi / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Química / Made available in DSpace on 2018-08-18T17:27:36Z (GMT). No. of bitstreams: 1 Maretto_DaniloAlthmann_D.pdf: 2617064 bytes, checksum: 1ebea2b6ab73ef552155cd9b79b6fd1b (MD5) Previous issue date: 2011 / Resumo: O objetivo desta tese de doutorado foi de utilizar o algoritmo Máquinas de Vetores de Suporte (SVM) em problemas de classificação e calibração, onde algoritmos mais tradicionais (SIMCA e PLS, respectivamente) encontram problemas. Foram realizadas quatro aplicações utilizando dados de espectroscopia no infravermelho. Na primeira o SVM se mostrou ser uma ferramenta mais indicada para a determinação de Carbono e Nitrogênio em solo por NIR, quando estes elementos estão em solos sem que se saiba se há ou não a presença do mineral gipsita, obtendo concentrações desses elementos com erros consideravelmente menores do que a previsão feita pelo PLS. Na determinação da concentração de um mineral em polímero por NIR, que foi a segunda aplicação, o PLS conseguiu previsões com erros aceitáveis, entretanto, através da análise do teste F e o gráfico de erros absolutos das previsões, foi possível concluir que o modelo SVM conseguiu chegar a um modelo mais ajustado. Na terceira aplicação, que consistiu na classificação de bactérias quanto às condições de crescimento (temperaturas 30 ou 40°C e na presença ou ausência de fosfato) por MIR, o SIMCA não foi capaz de classificar corretamente a grande maioria das amostras enquanto o SVM produziu apenas uma previsão errada. E por fim, na última aplicação, que foi a diferenciação de nódulos cirróticos e de hepatocarcinoma por microespectroscopia MIR, a taxa das previsões corretas para os conjuntos de validação do SVM foram maiores do que do SIMCA. Nas quatro aplicações o SVM produziu resultados melhores do que o SIMCA e o PLS, mostrando que pode ser uma alternativa aos métodos mais tradicionais de classificação e calibração multivariada / Abstract: The objective of this thesis was to use the algorithm Support Vector Machines (SVM) in problems of classification and calibration, where more traditional algorithms (SIMCA and PLS, respectively) present problems. Four applications were developed using data for infrared spectra. In the first one, the SVM proved to be a most suitable tool for determination of carbon and nitrogen in soil by NIR, when these elements are in soils without knowledge whether or not the presence of the gypsum mineral, obtaining concentrations of these elements with errors considerably smaller than the estimated by the PLS. In the determination of the concentration of a mineral in a polymer by NIR, which was the second application, the PLS presented predictions with acceptable errors, however, by examining the F test and observing absolute errors of predictions, it was concluded that the SVM was able to reach a more adjusted model. In the third application, classification of bacteria on the different growth conditions (temperatures 30 or 40 ° C and in the presence or absence of phosphate) by MIR, the SIMCA was not able to correctly classify the majority of the samples while the SVM produced only one false prediction. Finally, in the last application, which was the differentiation of cirrhotic nodules and Hepatocellular carcinoma by infrared microspectroscopy, the rate of correct predictions for the validation of sets of SVM was higher than the SIMCA. In the four applications SVM produced better results than SIMCA and PLS, showing that it can be an alternative to the traditional algorithms for classification and multivariate calibration / Doutorado / Quimica Analitica / Doutor em Ciências

Under high irradiation, a fourth state of matter named plasma can be obtained. Plasmas emit electromagnetic radiation that can be recorded in the form of spectra for spectroscopic elemental analysis. With the advent of lasers in the 1960s, spectroscopists realized that lasers could be used simultaneously as a source of energy and excitation to create plasmas. The use of a laser to ignite a plasma subsequently led to laser-induced breakdown spectroscopy (LIBS), an optical emission spectroscopy capable of analyzing samples in various states (solids, liquids, gases) with minimal sample preparation, rapid feedback, and endowed with in situ capability. In this dissertation, studies of LIBS for multi-elemental analysis and geological applications are reported. LIBS was applied to cosmetic powders for elemental analysis, screening and classification based on the raw material used. Principal component analysis (PCA) and internal standardization were used. The intensity ratios of Mg/Si and Fe/Si observed in talcum powder show that these two ratios could be used as indicators of the potential presence of asbestos. The feasibility of LIBS for the analysis of gasification slags was investigated and results compared with those of inductively-coupled plasma−optical emission spectrometry (ICP-OES). The limits of detection for Al, Ca, Fe, Si and V were determined. The matrix effect was studied using an internal standard and PLS-R. Apart from V, prediction results were closed to those of ICP-OES with accuracy within 10%. Elemental characterization of outcrop geological samples from the Marcellus Shale Formation was also carried out. The matrix effect was substantially reduced. The limits of detection obtained for Si, Al, Ti, Mg, Ca and C were determined. The relative errors of LIBS measurements are in the range of 1.7 to 12.6%. Gate delay and laser pulse energy, have been investigated in view of quantitative analysis of variation of trace elements in a high-pressure environment. Optimization of these parameters permits obtaining underwater plasma emission of calcium with quantitative results on the order of 30 ppm within a certain limit of increased pressure. Monitoring the variation of the trace elements can predict changes in the chemical composition in carbon sequestration reservoir.