999 resultados para Análises clínicas. Espectroscopia no infravermelho próximo. Calibração multivariada
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In this work, the quantitative analysis of glucose, triglycerides and cholesterol (total and HDL) in both rat and human blood plasma was performed without any kind of pretreatment of samples, by using near infrared spectroscopy (NIR) combined with multivariate methods. For this purpose, different techniques and algorithms used to pre-process data, to select variables and to build multivariate regression models were compared between each other, such as partial least squares regression (PLS), non linear regression by artificial neural networks, interval partial least squares regression (iPLS), genetic algorithm (GA), successive projections algorithm (SPA), amongst others. Related to the determinations of rat blood plasma samples, the variables selection algorithms showed satisfactory results both for the correlation coefficients (R²) and for the values of root mean square error of prediction (RMSEP) for the three analytes, especially for triglycerides and cholesterol-HDL. The RMSEP values for glucose, triglycerides and cholesterol-HDL obtained through the best PLS model were 6.08, 16.07 e 2.03 mg dL-1, respectively. In the other case, for the determinations in human blood plasma, the predictions obtained by the PLS models provided unsatisfactory results with non linear tendency and presence of bias. Then, the ANN regression was applied as an alternative to PLS, considering its ability of modeling data from non linear systems. The root mean square error of monitoring (RMSEM) for glucose, triglycerides and total cholesterol, for the best ANN models, were 13.20, 10.31 e 12.35 mg dL-1, respectively. Statistical tests (F and t) suggest that NIR spectroscopy combined with multivariate regression methods (PLS and ANN) are capable to quantify the analytes (glucose, triglycerides and cholesterol) even when they are present in highly complex biological fluids, such as blood plasma
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The application of analytical procedures based on multivariate calibration models has been limited in several areas due to requirements of validation and certification of the model. Procedures for validation are presented based on the determination of figures of merit, such as precision (mean, repeatability, intermediate), accuracy, sensitivity, analytical sensitivity, selectivity, signal-to-noise ratio and confidence intervals for PLS models. An example is discussed of a model for polymorphic purity control of carbamazepine by NIR diffuse reflectance spectroscopy. The results show that multivariate calibration models can be validated to fulfill the requirements imposed by industry and standardization agencies.
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The aim of this manuscript was to show the basic concepts and practical application of Partial Least Squares (PLS) as a tutorial, using the Matlab computing environment for beginners, undergraduate and graduate students. As a practical example, the determination of the drug paracetamol in commercial tablets using Near-Infrared (NIR) spectroscopy and Partial Least Squares (PLS) regression was shown, an experiment that has been successfully carried out at the Chemical Institute of Campinas State University for chemistry undergraduate course students to introduce the basic concepts of multivariate calibration in a practical way.
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This work is combined with the potential of the technique of near infrared spectroscopy - NIR and chemometrics order to determine the content of diclofenac tablets, without destruction of the sample, to which was used as the reference method, ultraviolet spectroscopy, which is one of the official methods. In the construction of multivariate calibration models has been studied several types of pre-processing of NIR spectral data, such as scatter correction, first derivative. The regression method used in the construction of calibration models is the PLS (partial least squares) using NIR spectroscopic data of a set of 90 tablets were divided into two sets (calibration and prediction). 54 were used in the calibration samples and the prediction was used 36, since the calibration method used was crossvalidation method (full cross-validation) that eliminates the need for a validation set. The evaluation of the models was done by observing the values of correlation coefficient R 2 and RMSEC mean square error (calibration error) and RMSEP (forecast error). As the forecast values estimated for the remaining 36 samples, which the results were consistent with the values obtained by UV spectroscopy
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In this work calibration models were constructed to determine the content of total lipids and moisture in powdered milk samples. For this, used the near-infrared spectroscopy by diffuse reflectance, combined with multivariate calibration. Initially, the spectral data were submitted to correction of multiplicative light scattering (MSC) and Savitzsky-Golay smoothing. Then, the samples were divided into subgroups by application of hierarchical clustering analysis of the classes (HCA) and Ward Linkage criterion. Thus, it became possible to build regression models by partial least squares (PLS) that allowed the calibration and prediction of the content total lipid and moisture, based on the values obtained by the reference methods of Soxhlet and 105 ° C, respectively . Therefore, conclude that the NIR had a good performance for the quantification of samples of powdered milk, mainly by minimizing the analysis time, not destruction of the samples and not waste. Prediction models for determination of total lipids correlated (R) of 0.9955, RMSEP of 0.8952, therefore the average error between the Soxhlet and NIR was ± 0.70%, while the model prediction to content moisture correlated (R) of 0.9184, RMSEP, 0.3778 and error of ± 0.76%
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This work was developed with the objective of proposing a simple, fast and versatile methodological routine using near-infrared spectroscopy (NIR) combined with multivariate analysis for the determination of ash content, moisture, protein and total lipids present in the gray shrimp (Litopenaeus vannamei ) which is conventionally performed gravimetrically after ashing at 550 ° C gravimetrically after drying at 105 ° C for the determination of moisture gravimetrically after a Soxhlet extraction using volumetric and after digestion and distillation Kjedhal respectively. Was first collected the spectra of 63 samples processed boiled shrimp Litopenaeus vannamei species. Then, the determinations by conventional standard methods were carried out. The spectra centered average underwent multiplicative scattering correction of light, smoothing Saviztky-Golay 15 points and first derivative, eliminated the noisy region, the working range was from 1100,36 to 2502,37 nm. Thus, the PLS models for predicting ash showed R 0,9471; 0,1017 and RMSEP RMSEC 0,1548; Moisture R was 0,9241; 2,5483 and RMSEP RMSEC 4,1979; R protein to 0,9201; 1,9391 and RMSEP RMSEC 2,7066; for lipids R 0,8801; 0,2827 and RMSEP RMSEC 0,2329 So that the results showed that the relative errors found between the reference method and the NIR were small and satisfactory. These results are an excellent indication that you can use the NIR to these analyzes, which is quite advantageous, since conventional techniques are time consuming, they spend a lot of reagents and involve a number of professionals, which requires a reasonable runtime while after the validation of the methodology execution using NIR reduces all this time to a few minutes, saving reagents, time and without waste generation, and that this is a non-destructive technique.
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O objetivo deste estudo foi desenvolver modelos de calibração multivariada, com espectros infravermelhos próximos (NIR), para predição de qualidade da madeira e da polpa celulósica kraft de Eucalyptus. Foram utilizadas 30 amostras de madeira, em forma de cavacos, e 116 amostras de polpa kraft de Eucalyptus. Os cavacos foram utilizados com dois teores de umidade (33% e 10%). Nos cavacos mais úmidos, foram obtidas as seguintes correlações: 97% na densidade básica, 84% no teor de extrativos e 93% no teor de lignina. Nos cavacos mais secos, as correlações foram: 97% na densidade básica, 92% no teor de extrativos e 90% na lignina. Para a predição de qualidade das polpas, foram obtidos espectros NIR de folhas de celulose e das polpas na forma original (desagregada). Os modelos com espectros obtidos na folha de celulose apresentaram correlações de 94% no número kappa, 93% na viscosidade da polpa, 90% no rendimento depurado e 86% no teor de ácido hexenurônico. Os modelos com espectros obtidos na polpa desagregada apresentaram as seguintes correlações: 90% no número kappa, 91% na viscosidade da polpa, 88% no rendimento depurado e 85% no teor de ácidos hexenurônicos. Os resultados demonstraram a viabilidade da aplicação da técnica de modelagem das características da madeira e da polpa celulósica, utilizando-se espectros NIR.
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The aim of this study was to evaluate the potential of near-infrared reflectance spectroscopy (NIRS) as a rapid and non-destructive method to determine the soluble solid content (SSC), pH and titratable acidity of intact plums. Samples of plum with a total solids content ranging from 5.7 to 15%, pH from 2.72 to 3.84 and titratable acidity from 0.88 a 3.6% were collected from supermarkets in Natal-Brazil, and NIR spectra were acquired in the 714 2500 nm range. A comparison of several multivariate calibration techniques with respect to several pre-processing data and variable selection algorithms, such as interval Partial Least Squares (iPLS), genetic algorithm (GA), successive projections algorithm (SPA) and ordered predictors selection (OPS), was performed. Validation models for SSC, pH and titratable acidity had a coefficient of correlation (R) of 0.95 0.90 and 0.80, as well as a root mean square error of prediction (RMSEP) of 0.45ºBrix, 0.07 and 0.40%, respectively. From these results, it can be concluded that NIR spectroscopy can be used as a non-destructive alternative for measuring the SSC, pH and titratable acidity in plums
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Diversas técnicas analíticas podem ser usadas na determinação carbono do solo, predominando no Brasil métodos baseados na oxidação da matéria orgânica na presença de dicromato de potássio em meio ácido ou na análise elementar. O objetivo deste trabalho foi avaliar a viabilidade da estimativa do teor de carbono em solos da bacia do Acre por meio de espectroscopia NIR combinada com calibração por análise multivariada. Foram utilizadas 190 amostras de solos, coletadas em diversas localidades da bacia do Acre, para testar a espectroscopia NIR na determinação de teor de carbono no solo, comparativamente aos métodos de oxidação por dicromato em meio ácido e a análise elementar. Tomando-se como referência o método do analisador elementar, verifica-se que o método da oxidação recuperou, em média, 63,8 % do carbono determinado pelo método de referência. Sugere-se que para determinação do teor total de carbono solo a partir do método da oxidação seja adotado o coeficiente de 1,55 para corrigir os valores para o total de carbono do solo. Quanto ao uso da espectroscopia NIR, o modelo desenvolvido para análise de carbono de solos da bacia do Acre por espectroscopia NIR apresentou classificação boa segundo os valores de R e classificação excelente segundo os valores de RMSEC < RMSEP e RPD, tendo como referência o carbono determinado pelo método da análise elementar.
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O tema “Plataforma smartphone para biossensores de Espectroscopia de Infravermelho Próximo, surge no âmbito da instrumentação médica, na área das BCI – Brain Computer Interfaces, devido à necessidade de encontrar um dispositivo portátil, de custo acessível e elevada performance que permita obter informação acerca da actividade neuronal do córtex motor no decorrer duma determinada tarefa. O objectivo do trabalho consiste no desenvolvimento duma sonda capaz de detectar as alterações hemodinâmicas que ocorrem no córtex, bem como toda a instrumentação inerente à aquisição do sinal e transmissão dos dados para um computador, a análise dos dados e por fim o desenvolvimento de uma aplicação em Android para visualização dos resultados. Foi desenvolvida uma banda para a cabeça, composta pela sonda NIRS: LEDs (Light-Emiting Diodes) de 940nm e 660nm e os respectivos fototransístores de detecção, bem como toda a electrónica de condicionamento do sinal captado. Num módulo à parte, alimentado por duas baterias de 9V, encontram-se os circuitos electrónicos onde é possível regular ganhos de amplificação e offsets. Os dados foram adquiridos pelo microcontrolador Arduíno Uno, usando uma taxa de amostragem de 50Hz em cada um dos dois canais utilizados. O controlo do Arduíno foi feito utilizando o LabVIEW. Para o processamento dos dados, visualização e cálculo das concentrações de oxi e desoxi-hemoglobina no sangue recorreu-se ao Matlab. O sistema foi calibrado com recurso a um oxímetro de pulso clínico usado em cinco indivíduos saudáveis. Finalmente o sistema foi testado ao colocar-se o sensor NIRS sobre o córtex motor esquerdo de nove indivíduos saudáveis destros, fazendo-se uma aquisição de dados durante dois minutos. Utilizou-se um paradigma de 10s de repouso seguido de 10s a abrir e fechar a mão. O sistema NIRS conseguiu medir as alterações que ocorrem nas concentrações de oxi e desoxi-hemoglobina devido à actividade motora de abrir e fechar a mão. Dado o princípio físico ser o mesmo do dos oxímetros convencionais, conseguiu-se ainda medir com sucesso a frequência cardíaca e a saturação percentual de oxigénio após a calibração do sensor. As medidas podem ser visualizadas numa aplicação desenvolvida para o Android. Os resultados sugerem que com esta abordagem, este tipo de dispositivo pode estar disponível a baixo custo quer para doentes quer para indivíduos saudáveis, por exemplo em aplicações de telemóvel.
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A chemical process optimization and control is strongly correlated with the quantity of information can be obtained from the system. In biotechnological processes, where the transforming agent is a cell, many variables can interfere in the process, leading to changes in the microorganism metabolism and affecting the quantity and quality of final product. Therefore, the continuously monitoring of the variables that interfere in the bioprocess, is crucial to be able to act on certain variables of the system, keeping it under desirable operational conditions and control. In general, during a fermentation process, the analysis of important parameters such as substrate, product and cells concentration, is done off-line, requiring sampling, pretreatment and analytical procedures. Therefore, this steps require a significant run time and the use of high purity chemical reagents to be done. In order to implement a real time monitoring system for a benchtop bioreactor, these study was conducted in two steps: (i) The development of a software that presents a communication interface between bioreactor and computer based on data acquisition and process variables data recording, that are pH, temperature, dissolved oxygen, level, foam level, agitation frequency and the input setpoints of the operational parameters of the bioreactor control unit; (ii) The development of an analytical method using near-infrared spectroscopy (NIRS) in order to enable substrate, products and cells concentration monitoring during a fermentation process for ethanol production using the yeast Saccharomyces cerevisiae. Three fermentation runs were conducted (F1, F2 and F3) that were monitored by NIRS and subsequent sampling for analytical characterization. The data obtained were used for calibration and validation, where pre-treatments combined or not with smoothing filters were applied to spectrum data. The most satisfactory results were obtained when the calibration models were constructed from real samples of culture medium removed from the fermentation assays F1, F2 and F3, showing that the analytical method based on NIRS can be used as a fast and effective method to quantify cells, substrate and products concentration what enables the implementation of insitu real time monitoring of fermentation processes
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The objective of this study was to evaluate the potential of near infrared spectroscopy (NIRS) associated with multivariate statistics to distinguish coal produced from wood of planted and native forests. Timber forest species from the C errado (Cedrela sp., Aspidosperma sp., Jacaranda sp. and unknown species) and Eucalyptus clones from forestry companies (Vallourec and Cenibra) were carbonized in the final temperatures of 300, 500 and 700°C. In each heat treatment were carbonized 15 specimens of each vegetal material totaling 270 samples (3 treatments x 15 reps x 6 materials) produced in 18 carbonization (3 treatments x 6 materials). The acquisition of the spectra of coals in the near infrared using a spectrometer was performed. Principal Component Analysis (PCA) and Partial Least Squares Regression (PLS-R) were carried out in the spectra. NIR Spectroscopy associated with PCA was not able to differentiate charcoals produced from native and planted woods when utilizing all carbonized samples at different temperatures in the same analysis; The PCA of all charcoals was able to distinguish the samples depending on temperature in which they were carbonized. However, the separation of native and planted charcoal was possible when the samples were analyzed separately by final temperature. The prediction of native or planted classes by PLS-R presented better performance for samples carbonized at 300°C followed by those at 500°C, 700°C and for all together.
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Pós-graduação em Química - IQ
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Pós-graduação em Alimentos e Nutrição - FCFAR