Desenvolvimento, validação, avaliação da incerteza de medição e análise de custos de método baseado em espectroscopia no infravermelho e análise multivariada para previsão de parâmetros de qualidade de amostras de E85

The use of ethanol as fuel can reduce emissions of greenhouse gases and dependence on fossil fuels, as it can be obtained from renewable sources. However, this biofuel can present problems of ignition and driving in areas with cold climates due to lower vapor pressure in relation to gasoline. To avoid such problems, ethanol can be mixed with in various proportions (50-85% in Europe and the United States). These mixtures are commercially known as E85. Other parameters (such as methanol and water content) are also important to ensure the quality of the fuel and are fixed by international standards. Thus, the present study shows the development, validation and estimation of the uncertainty of analytical methods using spectroscopy in the infrared region (middle and near) and multivariate regression, which allow quantifying levels of ethanol, methanol, oil and water in E85 samples prepared in laboratory. The validation was made by calculating the figures of merit using the concept of net analytical signal (NAS). Thus, the proposed methods were able to quantify the levels of ethanol, methanol, oil and water linearly, free of systematic errors, with accuracy of 0.2% (m/m), 0.03% (m/m), 0.2% (m/m) and 0.03% (m/m), accuracy of 0.3% (m/m), 0.02% (m/m), 0.2% (m/m) and 0.02% (m/m), respectively, and measuring intervals covering whole range of parameters established by the standards. Furthermore, pseudounivariate forms of multivariate regression models (using NAS) were used to estimate uncertainty of measuring methods, allowing calculations in accordance with the Guide to the Expression of Uncertainty in Measurement and also Monte Carlo simulation.O uso do etanol como combustível pode reduzir as emissões dos gases do efeito estufa e a dependência de combustíveis fósseis, já que pode ser obtido de fontes renováveis. Entretanto, este biocombustível pode apresentar problemas de ignição e de dirigibilidade em regiões com clima frio devido à menor pressão de vapor em relação à gasolina. Para evitar tais problemas, pode-se misturar etanol à gasolina em várias proporções (50-85% na Europa e nos Estados Unidos), essas misturas são comercialmente denominadas de E85. Outros parâmetros (como o teor de metanol e de água) também são importantes para garantir a qualidade do combustível e estão previstos em normas internacionais. Sendo assim, o presente trabalho mostra o desenvolvimento, a validação e a estimativa de incerteza de métodos analíticos, utilizando espectroscopia na região do infravermelho (médio e próximo) e regressão multivariada, que permitem quantificar os teores de etanol, metanol, hidrocarbonetos e água em amostras de E85 preparadas em laboratório. A validação se deu através do cálculo das figuras de mérito utilizando o conceito de sinal analítico líquido (NAS). Com isso, foi possível observar que os métodos propostos são capazes de quantificar os teores de etanol, metanol, hidrocarbonetos e água de forma linear, livre de erros sistemáticos, com precisão de 0,2% (m/m), 0,03% (m/m), 0,2% (m/m) e 0,03% (m/m), exatidão de 0,3% (m/m), 0,02% (m/m), 0,2% (m/m) e 0,02% (m/m), respectivamente, e com intervalos de medição cobrindo toda faixa dos parâmetros estabelecidos pelas normas. Além disso, as formas pseudounivariadas dos modelos de regressão multivariada (utilizando o NAS) foram empregadas na estimativa da incerteza de medição dos métodos, permitindo os cálculos de acordo com a abordagem do Guia para Incerteza de Medição e também por simulação de Monte Carlo

GC Fingerprints Coupled to Pattern-Recognition Multivariate SIMCA Chemometric Analysis for Brazilian Gasoline Quality Studies

ASTM D6729 gas chromatographic fingerprinting coupled to pattern-recognition multivariate soft independent modeling of class analogy (SIMCA) chemometric analysis provides an original and alternative approach to screening Brazilian commercial gasoline quality. SIMCA, was performed on gas chromatographic fingerprints to classify the quality of representative commercial gasoline samples selected by hierarchical cluster analysis and collected over a 5 month period from gas stations in So Paulo State, Brazil. Following an optimized ASTM D6729 gas chromatographic-SIMCA algorithm, it was possible to correctly classify the majority of commercial gasoline samples. The method could be employed for rapid monitoring to discourage adulteration.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Rapid and Simultaneous Prediction of Eight Diesel Quality Parameters through ATR-FTIR Analysis

Quality assessment of diesel fuel is highly necessary for society, but the costs and time spent are very high while using standard methods. Therefore, this study aimed to develop an analytical method capable of simultaneously determining eight diesel quality parameters (density; flash point; total sulfur content; distillation temperatures at 10% (T10), 50% (T50), and 85% (T85) recovery; cetane index; and biodiesel content) through attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and the multivariate regression method, partial least square (PLS). For this purpose, the quality parameters of 409 samples were determined using standard methods, and their spectra were acquired in ranges of 4000–650 cm−1. The use of the multivariate filters, generalized least squares weighting (GLSW) and orthogonal signal correction (OSC), was evaluated to improve the signal-to-noise ratio of the models. Likewise, four variable selection approaches were tested: manual exclusion, forward interval PLS (FiPLS), backward interval PLS (BiPLS), and genetic algorithm (GA). The multivariate filters and variables selection algorithms generated more fitted and accurate PLS models. According to the validation, the FTIR/PLS models presented accuracy comparable to the reference methods and, therefore, the proposed method can be applied in the diesel routine monitoring to significantly reduce costs and analysis time

Enzymatic modification of grapeseed (Vitis vinifera L.) oil aiming to obtain dietary triacylglycerols in a batch reactor

Structured lipids (SL) are chemically or enzymatically modified oils and fats with respect to their original fatty acid composition or position in acylglycerols. These compounds present improved functional or nutraceutical properties. The present work aimed at the enzymatic synthesis of SL, MLM-type dietary triacylglycerols, that is, those with medium chain fatty acids (M) at the sn-1 and sn-3 positions, and long chain fatty acids (L), in the internal position of the triacylglycerol. Grapeseed oil was selected based on its composition rich in unsaturated fatty acids, principally linoleic acid. This oil was submitted to batch acidolysis with medium chain fatty acids (caprylic or capric) in solvent-free media. Reactions were catalyzed by different immobilized commercial lipases, namely: Lipozyme TL IM® (Thermomyces lanuginosa lipase), Lipozyme RM IM® (Rhizomucor miehei lipase) and Novozym 435® (Candida antarctica lipase B). The incorporation degree (ID) ranged from 23.62 ± 1.34 to 34.53 ± 0.05 mol%, after 24 h reaction at 45 °C, using a molar ratio (MR) fatty acid:oil of 2:1. The best results were obtained using capric acid and Lipozyme RM IM® lipase (34.53 ± 0.05 mol%). In the experimental design, the influence of MR and temperature on ID were evaluated. ID increased with MR and T and was fitted to a saddle-like surface.info:eu-repo/semantics/publishedVersio

Multivariate calibrations on H-1 NMR profiles for prediction of physicochemical parameters of Brazilian commercial gasoline

Brazilian commercial gasoline follows a rigid quality control, covered by Regulation ANP 309 and following international analytical protocols, such as ASTM and ABNT. Each property is a complicated function of the gasoline chemical composition, which would be represented by diverse types of mathematical correlations. However, these correlations are not adjusted to Brazilian gasoline, whose chemical composition is modified by anhydrous ethanol addition. The purpose of this work is to find correlations using PLS regressions, between H-1 NMR Brazilian gasoline fingerprintings and physicochemical parameters, such as relative density, distillation curve, octane numbers, hydrocarbons compositions (olefins, aromatics and saturated) and anhydrous ethanol and benzene. One hundred and fifty representative gasoline samples, collected randomly from different gas stations, were analyzed following ASTM/ABNT analytical protocols. All H-1 NMR spectroscopic fingerprintings, reported in parts per million (ppm) relative to residual proton signals of CDCl3 at 7.24 ppm, were acquired on a Varian INOVA spectrometer 500 MHz. FIDs were zero filled and Fourier transformed. Data matrix, composed by H-1 NMR chemical shifts and physicochemical parameters, was constructed and imported into Pirouette (R) 3.11 software for PLS regression. H-1 NMR fingerprinting of 100 gasoline samples were employed in the training set and 50 samples formed the prediction set. RMSEC and RMSEP were the parameters considered to select the "best model". H-1 NMR-PLS models results in good prediction capability when compared to repeatability and reproducibility of ASTM/ABNT analytical protocols. H-1 NMR-PLS multivariate regressions supplies an alternative analytical procedure for commercial automotive gasoline quality control. (C) 2012 Elsevier Ltd. All rights reserved.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Carbon Nuclear Magnetic Resonance Spectroscopic Profiles coupled to Partial Least-Squares Multivariate Regression for Prediction of Several Physicochemical Parameters of Brazilian Commercial Gasoline

Brazilian commercial gasoline follows a rigid quality control, regulated by Brazilian Government Petroleum, Natural Gas, and Biofuels Agency, ANP, following international analytical protocols, such as ASTM and ABNT, covered by Regulation ANP No. 309. Each property is a complicated function of the gasoline chemical composition, which would be represented by diverse types of mathematical correlations. However, these correlations are not adjusted to Brazilian gasoline, whose chemical composition is modified by anhydrous ethanol addition. The purpose of this work is to find correlations, using partial least-squares (PLS) regressions, between C-13 NMR Brazilian gasoline fingerprintings and several physicochemical parameters, such as relative density, distillation curve (temperatures related to 10, 50, and 90% of distilled volume, final boiling point and residue), octane numbers (motor and research octane number and antiknock index), hydrocarbon compositions (olefins, aromatics, and saturated) and anhydrous ethanol and benzene. 150 representative gasoline samples, collected randomly from different gas stations, were analyzed following international analytical protocols. All C-13 NMR spectroscopic fingerprintings, reported in parts per million (ppm), FIDs (free induction decays) were zero filled and Fourier transformed. A data matrix, composed of C-13 NMR chemical shifts and physicochemical parameters, was constructed and used in PLS regression. C-13 NMR fingerprinting of 100 gasoline samples were employed in the training set, and 50 samples formed the prediction set. In C-13 NMR-PLS models, root-mean square error of calibration (RMSEC) and prediction (RMSEP) were the mains parameters considered to select the "best model", which shown results roughly similar in magnitude to the repeatability and reproducibility of ASTM and NBR officials analytical protocols. C-13 NMR-PLS multivariate regression, as an alternative analytical methodology, offers an appealing procedure for commercial automotive gasoline quality control.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq