827 resultados para calibração de TDR


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Dilutions of methylmetacrylate ranging between 1 and 50 ppm were obtained from a stock solution of 1 ml of monomer in 100 ml of deionised water, and were analyzed by an absorption spectrophotometer in the UV-visible. Absorbance values were used to develop a calibration model based on the PLS, with the aim to determine new sample concentrations. The number of latent variables used was 6, with the standard errors of calibration and prediction found to be 0,048 ml/100 ml and 0,058 ml/100 ml. The calibration model was successfully used to calculate the concentration of monomer released in water, where complete dentures were kept for one hour after polymerization.

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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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A simple method was proposed for determination of paracetamol and ibuprofen in tablets, based on UV measurements and partial least squares. The procedure was performed at pH 10.5, in the concentration ranges 3.00-15.00 µg ml-1 (paracetamol) and 2.40-12.00 µg ml-1 (ibuprofen). The model was able to predict paracetamol and ibuprofen in synthetic mixtures with root mean squares errors of prediction of 0.12 and 0.17 µg ml-1, respectively. Figures of merit (sensitivity, limit of detection and precision) were also estimated. The results achieved for the determination of these drugs in pharmaceutical formulations were in agreement with label claims and verified by HPLC.

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Calibration transfer has received considerable attention in the recent literature. Several standardization methods have been proposed for transferring calibration models between equipments. The goal of this paper is to present a general revision of calibration transfer techniques. Basic concepts will be reviewed, as well as the main advantages and drawbacks of each technique. A case study based on a set of 80 NIR spectra of maize samples recorded on two different instruments is used to illustrate the main calibration transfer techniques (direct standardization, piecewise direct standardization, orthogonal signal correction and robust variable selection).

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In this work, the artificial neural networks (ANN) and partial least squares (PLS) regression were applied to UV spectral data for quantitative determination of thiamin hydrochloride (VB1), riboflavin phosphate (VB2), pyridoxine hydrochloride (VB6) and nicotinamide (VPP) in pharmaceutical samples. For calibration purposes, commercial samples in 0.2 mol L-1 acetate buffer (pH 4.0) were employed as standards. The concentration ranges used in the calibration step were: 0.1 - 7.5 mg L-1 for VB1, 0.1 - 3.0 mg L-1 for VB2, 0.1 - 3.0 mg L-1 for VB6 and 0.4 - 30.0 mg L-1 for VPP. From the results it is possible to verify that both methods can be successfully applied for these determinations. The similar error values were obtained by using neural network or PLS methods. The proposed methodology is simple, rapid and can be easily used in quality control laboratories.

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This paper is a translation of an IUPAC document by K. Danzer, M. Otto and L. A. Currie (Pure Appl. Chem., 2004, 76(6), 1215-1225). Its goal is to establish a uniform and meaningful standard for terminology (in Portuguese), notation, and formulation concerning multispecies calibration in analytical chemistry. Calibration in analytical chemistry refers to the relation between sample domain and measurement domain (signal domain) expressed by an analytical function x = f s (Q) representing a pattern of chemical species Q and their amounts or concentrations x in a given test sample and a measured function y = f (z) that may be a spectrum, chromatogram, etc. Simultaneous multispecies analyses are carried out mainly by spectroscopic and chromatographic methods in a more or less selective way. For the determination of n species Qi (i=1,2, ..., n), at least n signals must be measured which should be well separated in the ideal case. In analytical practice, the situation can be different.

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The validation of an analytical procedure must be certified through the determination of parameters known as figures of merit. For first order data, the acuracy, precision, robustness and bias is similar to the methods of univariate calibration. Linearity, sensitivity, signal to noise ratio, adjustment, selectivity and confidence intervals need different approaches, specific for multivariate data. Selectivity and signal to noise ratio are more critical and they only can be estimated by means of the calculation of the net analyte signal. In second order calibration, some differentes approaches are necessary due to data structure.

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The most widespread literature for the evaluation of uncertainty - GUM and Eurachem - does not describe explicitly how to deal with uncertainty of the concentration coming from non-linear calibration curves. This work had the objective of describing and validating a methodology, as recommended by the recent GUM Supplement approach, to evaluate the uncertainty through polynomial models of the second order. In the uncertainty determination of the concentration of benzatone (C) by chromatography, it is observed that the uncertainty of measurement between the methodology proposed and Monte Carlo Simulation, does not diverge by more than 0.0005 unit, thus validating the model proposed for one significant digit.

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The goal of this work is the development and validation of an analytical method for fast quantification of sibutramine in pharmaceutical formulations, using diffuse reflectance infrared spectroscopy and partial least square regression. The multivariate model was elaborated from 22 mixtures containing sibutramine and excipients (lactose, microcrystalline cellulose, colloidal silicon dioxide and magnesium stearate) and using fragmented (750-1150/ 1350-1500/ 1850-1950/ 2600-2900 cm-1) and smoothing spectral data. Using 10 latent variables, excellent predictive capacity were observed in the calibration (n=20, RMSEC=0.004, R= 0.999) and external validation (n=5, RMSEC= 9.36, R=0.999) phases. In the analysis of synthetic mixtures the precision (SD=3,47%) was compatible with the rules of the Agencia Nacional de Vigilância Sanitária (ANVISA-Brazil). In the analysis of commercial drugs good agreement was observed between spectroscopic and chromatographic methods.

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Multivariate models were developed using Artificial Neural Network (ANN) and Least Square - Support Vector Machines (LS-SVM) for estimating lignin siringyl/guaiacyl ratio and the contents of cellulose, hemicelluloses and lignin in eucalyptus wood by pyrolysis associated to gaseous chromatography and mass spectrometry (Py-GC/MS). The results obtained by two calibration methods were in agreement with those of reference methods. However a comparison indicated that the LS-SVM model presented better predictive capacity for the cellulose and lignin contents, while the ANN model presented was more adequate for estimating the hemicelluloses content and lignin siringyl/guaiacyl ratio.

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A multivariate spectrophotometric method was developed for analysis of kojic acid/hydroquinone associations in skin whitening cosmetics. The method is based on the reaction between kojic acid and Fe3+ and on the reduction of Fe3+ by hydroquinone and further complexation of Fe2+ with 1,10-phenanthroline. The multivariate model was developed by Partial Least Squares Regression (PLSR), using 25 synthetic mixtures and mean-centered spectral data (350-380 nm). The use of 3 (kojic acid) and 2 (hydroquinone) latent variables permits the observation of mean errors of about 5% in the external validation phase.

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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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The conventional curriculum of Analytical Chemistry undergraduate courses emphasizes the introduction of techniques, methods and procedures used for instrumental analysis. All these concepts must be integrated into a sound conceptual framework to allow students to make appropriate decisions. Method calibration is one of the most critical parameters that has to be grasped since most analytical techniques depend on it for quantitative analysis. The conceptual understanding of calibration is not trivial for undergraduate students. External calibration is widely discussed during instrumental analysis courses. However, the understanding of the limitations of external calibration to correct some systematic errors is not directly derived from laboratory examples. The conceptual understanding of other calibration methods (standard addition, matrix matching, and internal standard) is imperative. The aim of this work is to present a simple experiment using grains (beans, corn and chickpeas) to explore different types of calibration methods.

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Two simple and efficient procedures have been developed for the rapid simultaneous determination of compounds with mutual spectral interference (rifampicin (RIF) and isoniazid (INH)). The first method was based on the UV–Vis spectral signal (190–600 nm) of synthetic RIF and INH aqueous solutions, whereas the second method involved the visible spectral signal registered between 350 and 800 nm after the reaction of INH with a Cu2+/neocuproine complex. Both multivariate spectrophotometric methods show excellent prevision capacity, providing results that are statistically equivalent with those provided by the standard chromatographic procedure. The methods were validated according to criteria established by ANVISA, showing precision, accuracy and robustness compatible with the requirements for new analytical methods, additionally allowing the reduction of waste generation.

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O método dos mínimos quadrados parciais (PLS) foi aplicado aos dados obtidos por voltametria de onda quadrada para a determinação simultânea de ácido ascórbico (AA) e do co-fator b-nicotinamida adenina dinucleotídeo (NADH) em misturas sintéticas. As curvas voltamétricas foram obtidas em tampão fosfato 0,2 mol L-1 (pH 8,0). Foi possível verificar que o método PLS permite a determinação destes compostos simultaneamente nas condições escolhidas. Os resultados mostraram um erro relativo máximo de 1,7% para o NADH e 2,1 % para o AA. A metodologia proposta é simples e desenvolvimentos posteriores podem torná-la útil para análises in vivo.