Experimental design employed to square wave voltammetry response optimization for the glyphosate determination

The reduction peak current of the derivative N-nitroso of glyphosate herbicide was optimized by experimental design for their determination. The variables involved in the optimization of the square wave voltammetry (SWV) were: voltage step, amplitude, frequency, concentration of the supporting electrolyte and the area of the mercury drop electrode. A complete 2(5) factorial design was used to evaluate the effects of the variables. From the results obtained by the factorial design was set the three more important factors. These variables were evaluated with a central composite design. The conditions that proportionate the best voltammetric response were: 0.025 volt, 0.125 volt, 70 Hz, 1.25 mol L-1 e 0.60 mm² for step voltage, amplitude, frequency, electrolyte concentration and area of the mercury drop, respectively. In these conditions the operational range was from 0.050 up to 100.0 µg mL-1, the detection and quantification limits were 0.025 and 0.080 µg mL-1, respectively.A corrente de pico de redução do derivativo N-nitroso do herbicida glifosato foi otimizada por planejamentos experimentais, para sua determinação. As variáveis envolvidas na otimização da voltametria de onda quadrada (SWV) foram: incremento de voltagem, amplitude, freqüência, concentração do eletrólito de suporte e a área da gota de mercúrio. Um planejamento fatorial completo 2(5) foi usado para avaliar os efeitos das variáveis. Dos resultados obtidos foram escolhidos três dos fatores mais importantes. Estas variáveis foram estudadas com o planejamento composto central. As condições que proporcionaram a melhor resposta voltamétrica foram: 0,025 volt, 0,125 volt, 70 Hz, 1,25 mol L-1 e 0,60 mm² para o incremento de voltagem, amplitude, freqüência, concentração do eletrólito e área da gota de mercúrio, respectivamente. Nestas condições a faixa operacional foi de 0,050 a 100,0 µg mL-1, os limites de detecção e quantificação foram de 0,025 e 0,080 µg mL-1, respectivamente.865871Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES

Chemometrics II: spreadsheets for experimental design calculations, a tutorial

This work describes, through examples, a simple way to carry out experimental design calculations applying an spreadsheets. The aim of this tutorial is to introduce an alternative to sophisticated commercial programs that normally are too complex in data input and output. An overview of the principal methods is also briefly presented. The spreadsheets are suitable to handle different types of computations such as screening procedures applying factorial design and the optimization procedure based on response surface methodology. Furthermore, the spreadsheets are sufficiently versatile to be adapted to specific experimental designs.338350Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Multivariate calibration to determine phorbol esters in seeds of Jatropha curcas l. Using near infrared and ultraviolet spectroscopies

Link não abre. Favor verificar.The building of partial least squares (PLS) regression models using near infrared (NIR) and ultraviolet (UV) spectroscopies to estimate the concentrations of phorbol esters (PEs) in Jatropha curcas L. is presented. The models were built using two algorithms for variable selection, ordered predictors selection (OPS) and genetic algorithm (GA). Chromatographic analyses were performed to determine the concentrations of PEs. Spectral data were obtained from seeds and oil extract. The results of PLS models were performed by analyzing statistical parameters of quality such as root mean square error of prediction (RMSEP) and correlation coefficient of external predictions (Rp). The parameters obtained for NIR-PLS and UV-PLS models with OPS were respectively: RMSEP 0.48 and 0.22 mg g-1 and Rp 0.49 and 0.96. For GA were obtained, respectively: RMSEP 0.52 and 0.28 mg g-1 and Rp 0.12 and 0.95. The models built from seeds and oil extracts can be used respectively for screening and to accurately predict the PEs content. The OPS method provided simpler and more predictive models compared to those obtained by the selection of variables using the GA. Thus, the UV-PLS-OPS model can be used as an alternative method to quantification of PEs

Estimation of cellulose crystallinity of sugarcane biomass using near infrared spectroscopy and multivariate analysis methods

A method for estimation of sugarcane (Saccharum spp.) biomass crystallinity using near infrared spectroscopy (NIR) and partial least squares regression (PLS) as an alternative to the standard method using X-ray diffractometry (XRD) is proposed. Crystallinity was obtained using XRD from sugarcane bagasse. NIR spectra were obtained of the same material. PLS models were built using the NIR and crystallinity values. Cellulose crystallinity ranged from 50 to 81%. Two variable selection algorithms were applied to improve the predictive ability of models, i.e. (a) Ordered Predictors Selection (OPS) and (b) Genetic Algorithm. The best model, obtained with the OPS algorithm, presented values of correlation coefficient of prediction, root mean squared error of prediction and ratio of performance deviation equals to 0.92, 3.01 and 1.71, respectively. A scatter matrix among lignin, α-cellulose, hemicellulose, ash and crystallinity was built that showed that there was no correlation among these properties for the samples studied

New strategy for determination of anthocyanins, polyphenols and antioxidant capacity of Brassica oleracea liquid extract using infrared spectroscopies and multivariate regression

A new method was developed to determine the antioxidant properties of red cabbage extract (Brassica oleracea) by mid (MID) and near (NIR) infrared spectroscopies and partial least squares (PLS) regression. A 70% (v/v) ethanolic extract of red cabbage was concentrated to 9° Brix and further diluted (12 to 100%) in water. The dilutions were used as external standards for the building of PLS models. For the first time, this strategy was applied for building multivariate regression models. Reference analyses and spectral data were obtained from diluted extracts. The determinate properties were total and monomeric anthocyanins, total polyphenols and antioxidant capacity by ABTS (2,2-azino-bis(3-ethyl-benzothiazoline-6-sulfonate)) and DPPH (2,2-diphenyl-1-picrylhydrazyl) methods. Ordered predictors selection (OPS) and genetic algorithm (GA) were used for feature selection before PLS regression (PLS-1). In addition, a PLS-2 regression was applied to all properties simultaneously. PLS-1 models provided more predictive models than did PLS-2 regression. PLS-OPS and PLS-GA models presented excellent prediction results with a correlation coefficient higher than 0.98. However, the best models were obtained using PLS and variable selection with the OPS algorithm and the models based on NIR spectra were considered more predictive for all properties. Then, these models provided a simple, rapid and accurate method for determination of red cabbage extract antioxidant properties and its suitability for use in the food industry

Temporal decomposition sampling and chemical characterization of eucalyptus harvest residues using NIR spectroscopy and chemometric methods

Near-infrared (NIR) spectroscopy and chemometric methods were used to predict the chemical properties of decomposing eucalyptus harvest residues to better understand the decomposition process of these materials. Leaves, twigs, branches, and bark from a decomposition experimental set up in commercial plantations were sampled for one year. The contents of carbon (C), nitrogen (N), extractives (EX), acid-soluble lignin (SL), Klason insoluble lignin (KL) and holocellulose (HC) were determined by the reference method in the collected samples. Principal component analysis (PCA) was employed to distinguish the types of harvest residues throughout the decomposition period. Multi-residue regression models were built from the NIR spectra using partial least squares regression (PLS). Two feature selection methods, i.e., ordered predictors selection (OPS) and genetic algorithm (GA), were applied and compared. The OPS and GA did not differ statistically; however, compared with the GA, OPS was more computationally efficient and selected fewer variables. Using the PLS-OPS models, the root mean square errors of prediction (RMSEP) for C, N, EX, SL, KL and HC were 19.70, 0.08, 0.74, 0.39, 28.13 and 33.99, respectively, and the prediction correlations (Rp) for these properties were 0.94, 0.99, 0.99, 0.99, 0.96 and 0.98, respectively. PLS-discriminant analysis (PLS-DA) was used to classify the samples over the decomposition time and provided a good separation. Some mismatches obtained in the modeled classes were explained by the differences in the decomposition rate and changes in the chemical composition of the different harvest residue components that were evaluated. The results showed the feasibility of NIR spectroscopy and chemometric methods to evaluate the chemistry of decomposing eucalyptus harvest residues, indicating that these methods can be used as rapid and inexpensive alternatives to conventional methods to help understand the decomposition process

Direct conversion of glucose to 5-hydroxymethylfurfural using a mixture of niobic acid and niobium phosphate as a solid acid catalyst

The aim of this work was to optimize the acid-catalyzed conversion of glucose into 5-hydroxymethylfurfural (HMF) in an aqueous medium using niobic acid (NbO), niobium phosphate (NbP) and a mixture of both solid acid catalysts. A simplex-centroid mixture design was applied to optimize the mixture ratio between NbO and NbP. A central composite design was applied to process optimization. The studied variables were temperature (T), time (t) and substrate to catalyst weight ratio (RS/C). The mixture design revealed excellent glucose conversion (55%) and HMF selectivity (56%) when the weight ratio between NbO and NbP was equal to 1:1. The experimental results demonstrate that a mixture of both solid acids provides a better combination of Brønsted and Lewis acidity for effective glucose dehydration into HMF than each individual catalyst

Correlation of quantitative sensorial descriptors and chromatographic signals of beer using multivariate calibration strategies

In this study, two important sensorial parameters of beer quality – bitterness and grain taste – were correlated with data obtained after headspace solid phase microextraction – gas chromatography with mass spectrometric detection (HS-SPME–GC–MS) analysis. Sensorial descriptors of 32 samples of Pilsner beers from different brands were previously estimated by conventional quantitative descriptive analyses (QDA). Areas of 54 compounds systematically found in the HS-SPME–GC–MS chromatograms were used as input data. Multivariate calibration models were established between the chromatographic areas and the sensorial parameters. The peaks (compounds) relevant to build each multivariate calibration model were determined by genetic algorithm (GA) and ordered predictors selection (OPS), tools for variable selection. GA selected 11 and 15 chromatographic peak areas, for bitterness and grain taste, respectively; while OPS selected 17 and 16 compounds for the same parameters. It could be noticed that seven variables were commonly pointed out by both variable selection methods to bitterness parameter and 10 variables were commonly selected to grain taste attribute. The peak areas most significant to the evaluation of the parameters found by both variable selection methods fed to the PLS algorithm to find the proper models. The obtained models estimated the sensorial descriptors with good accuracy and precision, showing that the utilised approaches were efficient in finding the evaluated correlations. Certainly, the combination of proper chemometric methodologies and instrumental data can be used as a potential tool for sensorial evaluation of foods and beverages, allowing for fast and secure replication of parameters usually measured by trained panellists134316731681CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQSem informaçã