Instituto Nacional Of Ciencia E Tecnologia Em Bioanalitica Inct Bio

Campinas, Brazil

Instituto Nacional Of Ciencia E Tecnologia Em Bioanalitica Inct Bio

Campinas, Brazil
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Piantavini M.S.,Federal University of Paraná | Pontes F.L.D.,Federal University of Paraná | Weiss L.X.,Federal University of Paraná | Sena M.M.,Federal University of Minas Gerais | And 2 more authors.
Journal of the Brazilian Chemical Society | Year: 2015

Methods based on multivariate calibration and diffuse reflectance infrared Fourier transform (DRIFT) and ultraviolet (UV) spectroscopies were developed for the simultaneous determination of two veterinary pharmaceutical drugs, pyrantel pamoate and praziquantel, in commercial tablets. The best UV model was obtained with the full spectra, 200-400 nm, and partial least squares (PLS). The best DRIFT model was optimized by selecting the most predictive spectral regions with synergy interval PLS, 3998-3636 cm-1, 3274-1824 cm-1 and 1100-735 cm-1. Both methods were validated according to Brazilian and international guidelines through the estimate of figures of merit, such as trueness, precision, linearity, analytical sensitivity, bias and residual prediction deviation (RPD). These methods were applied to the determination of the drugs in three different veterinary formulations commercialized in the Brazilian market and the results were compared with high performance liquid chromatography (HPLC). DRIFT was considered more suitable for the quality control of these formulations, because it is faster, does not use solvents and does not generate chemical waste. ©2015 Sociedade Brasileira de Química.


Botelho B.G.,Federal University of Minas Gerais | Mendes B.A.P.,Instituto Mineiro Of Agropecuaria Ima | Sena M.M.,Federal University of Minas Gerais | Sena M.M.,Instituto Nacional Of Ciencia E Tecnologia Em Bioanalitica Inct Bio
Food Analytical Methods | Year: 2013

This paper proposed and validated robust diffuse reflectance near-infrared methods for the direct determination of fat and moisture in cow mozzarella cheeses using partial least squares regression. They were developed under the realistic conditions of routine analysis in a state laboratory of quality inspection control and were used for analyzing a great variety of mozzarella samples manufactured by different manufacturing procedures and originating from the whole state of Minas Gerais, Brazil (more than 100 different producers). A robust methodology was implemented, including the detection of outliers and the harmonization of the multivariate concepts with the traditional univariate guidelines. The models were constructed in the ranges from 38. 7 to 58. 0 % w/w on dry basis for fat and from 41. 5 to 55. 1 % w/w for moisture, providing root mean square errors of prediction of 2. 1 and 0. 9 %, respectively. Both methods were validated through the estimation of figures of merit, such as linearity, trueness, precision, analytical sensitivity, ruggedness, bias, and residual prediction deviation. Once the methods were adopted, their performances were monitored for approximately 1 year through control charts and were considered satisfactorily stable with prediction errors within the established limits. Beyond these specific methods, it was also pursued to present a complete methodology for multivariate analytical validation, an important aspect for the implementation of near-infrared spectroscopy methods in the routine of food quality inspection. © 2012 Springer Science+Business Media, LLC.


Botelho B.G.,Federal University of Minas Gerais | Mendes B.A.P.,Instituto Mineiro Of Agropecuaria Ima | Sena M.M.,Federal University of Minas Gerais | Sena M.M.,Instituto Nacional Of Ciencia E Tecnologia Em Bioanalitica Inct Bio
Quimica Nova | Year: 2013

This study developed and validated a method for moisture determination in artisanal Minas cheese, using near-infrared spectroscopy and partial-least-squares. The model robustness was assured by broad sample diversity, real conditions of routine analysis, variable selection, outlier detection and analytical validation. The model was built from 28.5-55.5% w/w, with a root-mean-square-error-of-prediction of 1.6%. After its adoption, the method stability was confirmed over a period of two years through the development of a control chart. Besides this specific method, the present study sought to provide an example multivariate metrological methodology with potential for application in several areas, including new aspects, such as more stringent evaluation of the linearity of multivariate methods.


Botelho B.G.,Federal University of Minas Gerais | De Assis L.P.,Federal University of Minas Gerais | Sena M.M.,Federal University of Minas Gerais | Sena M.M.,Instituto Nacional Of Ciencia E Tecnologia Em Bioanalitica Inct Bio
Food Chemistry | Year: 2014

This paper proposed a novel methodology for the quantification of an artificial dye, sunset yellow (SY), in soft beverages, using image analysis (RGB histograms) and partial least squares regression. The developed method presented many advantages if compared with alternative methodologies, such as HPLC and UV/VIS spectrophotometry. It was faster, did not require sample pretreatment steps or any kind of solvents and reagents, and used a low cost equipment, a commercial flatbed scanner. This method was able to quantify SY in isotonic drinks and orange sodas, in the range of 7.8-39.7 mg L-1, with relative prediction errors lower than 10%. A multivariate validation was also performed according to the Brazilian and international guidelines. Linearity, accuracy, sensitivity, bias, prediction uncertainty and a recently proposed tool, the β-expectation tolerance intervals, were estimated. The application of digital images in food analysis is very promising, opening the possibility for automation. © 2014 Elsevier Ltd. All rights reserved.


Botelho B.G.,Federal University of Minas Gerais | Reis N.,Federal University of Minas Gerais | Oliveira L.S.,Federal University of Minas Gerais | Sena M.M.,Federal University of Minas Gerais | Sena M.M.,Instituto Nacional Of Ciencia E Tecnologia Em Bioanalitica Inct Bio
Food Chemistry | Year: 2015

This paper proposed a new screening method for the simultaneous detection of five common adulterants in raw cow milk by using attenuated total reflectance (ATR) mid infrared spectroscopy and multivariate supervised classification (partial least squares discrimination analysis - PLSDA). The method was able to detect the presence of the adulterants water, starch, sodium citrate, formaldehyde and sucrose in milk samples containing from one up to five of these analytes, in the range of 0.5-10% w/v. A multivariate qualitative validation was performed, estimating specific figures of merit, such as false positive and false negative rates, selectivity, specificity and efficiency rates, accordance and concordance. The proposed method does not need any sample pretreatment, requires a small amount of sample (30 μL), is fast and simple, being suitable for the control of raw milk in a dairy industry or for the quality inspection of commercialized milk. © 2015 Elsevier Ltd. All rights reserved.


Rodrigues N.V.S.,Federal University of Minas Gerais | Cardoso E.M.,Federal University of Minas Gerais | Andrade M.V.O.,Federal University of Minas Gerais | Donnici C.L.,Federal University of Minas Gerais | And 2 more authors.
Journal of the Brazilian Chemical Society | Year: 2013

The aim of this article was to develop a chemometric methodology for determining the chemical profile of cocaine samples seized in Minas Gerais State, Brazil. The adulterant detection and the cocaine determination were performed by gas chromatography-mass spectrometry (GC-MS). Spectra of 91 samples were obtained by attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) and used to build an exploratory principal component analysis (PCA) model. The first principal component (PC1) discriminated samples of more purity from the more diluted/adulterated ones, which were characterized by the presence of lidocaine, caffeine and benzocaine. PC2 discriminated the two chemical forms of cocaine, hydrochloride and base. In addition, two supervised discriminant partial least-squares models (partial least-squares discriminant analysis, PLS-DA) were developed for classifying the samples according to dilution (above and below 15% m/m) and chemical form, with a rate of success that varied between 83 and 97%. The classification models constitute a simple, rapid and non-destructive tool, of great value for both forensic experts and criminal investigators. © 2013 Sociedade Brasileira de Química.

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