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

USE OF MULTIVARIATE EXPERIMENTAL DESIGNS FOR OPTIMIZING THE REDUCTIVE DEGRADATION OF AN AZO DYE IN THE PRESENCE OF REDOX MEDIATORS

The optimization of the anaerobic degradation of the azo dye Remazol golden yellow RNL was performed according to multivariate experimental designs: a 2² full-factorial design and a central composite design (CCD). The CCD revealed that the best incubation conditions (90% color removal) for the degradation of the azo dye (50 mg L- 1) were achieved with 350 mg L- 1 of yeast extract and 45 mL of anaerobic supernatant (free cell extract) produced from the incubation of 650 mg L- 1 of anaerobic microorganisms and 250 mg L- 1 of glucose. A first-order kinetics model best fit the experimental data (k = 0.0837 h- 1, R² = 0.9263)

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

OTIMIZAÇÃO MULTIVARIADA DE METODOLOGIA PARA DIGESTÃO DE MICROPARTÍCULAS POLIMÉRICAS CARREADORAS DE CÁTIONS METÁLICOS

There are not reports in the literature of digestion processes of metallic cation carrier microparticles. Multivariate experimental designs were utilized to ensure an appropriate digestion for further analysis by atomic absorption spectroscopy. It was used a heater plate and the polimeric degradation was evaluated utilizing an analyzer of total organic carbon (TOC). For the total carbon (TC) content, the full factorial design 24 pointed the significance of sample volume (VAm), in relation to the variables acid volume (VAc), temperature (T) and digestion time (t). However, according to the normal distribution graph it was noted a possible significance of the T and t effects. The same was observed for TOC, including the VAm × VAc × T effect suggestive of complex behavior. In the central composite design all variables were again studied and the VAM was significant, promoting a TC decreasing at the lower evaluated level. By ANOVA a quadratic model without lack of fit was found, with the significant quadratic term. The best digestion condition was: 5.00 mL of sample, 10.00 mL of nitric acid, 60 ºC and 90 min. The multivariate optimization allowed an efficient digestion, with the initial carbon concentration of 4.60 mg L-1 decreased to 0.55 mg L-1

Application of a multivariate exploratory analysis technique in the study of dissolved organic matter and metal Ions in waters from the eastern Quadrilátero Ferrífero, Brazil.

Amostras de água foram coletadas em 10 pontos em trechos do leste do Quadrilátero Ferrífero (QF), que é uma região mineira situada no sudeste do Brasil. Os objetivos deste estudo foram encontrar possíveis relações entre carbono orgânico dissolvido (COD), metais e outros parâmetros físico-químicos medidos utilizando a rede neural de Kohonen como ferramenta para analisar esses dados geoquímicos multivariados na área estudada. As análises físico-químicas foram feitas in situ e em laboratório, onde as concentrações de COD e vários íons metálicos foram determinadas. A rede de Kohonen permitiu a visualização e interpretação mais amigáveis dos dados, além de definir relações entre eles. Assim, para os dados analisados, foi verificada relação entre COD e Fe e um possível efeito da sazonalidade na distribuição das amostras. Possíveis evidências litológicas puderam ser detectadas pela análise exploratória, especialmente se considerados os elementos Ca, Mg, Mn e Sr.Water samples were collected at 10 points in parts of the eastern Quadrilátero Ferrífero (QF), located in a mining region in the southeast of Brazil. The aims of this study were to find possible relationships among dissolved organic carbon (DOC), metals and other parameters measured in the region studied and evaluate the Kohonen neural network as a tool to analyse this geochemical multivariate data set. Physico-chemical analyses were performed in situ and in the laboratory, where concentrations of DOC and a suite of metal ions were determined. The Kohonen neural network allowed an easier visualisation and interpretation of the results and helped to define the relationships among them. In this way, a relationship between DOC and Fe and a possible effect of seasonality on the distribution of the samples were indicated. Signs of lithology were detected in the analyses, especially considering the elements Ca, Mg, Mn and Sr

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

CNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOIn 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 panellists.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, respectivelywhile 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 panellists134316731681CNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGIC

MCR-ALS applied to the quantitative monitoring of the electrodegradation process of atrazine using UV spectra: comparative results with HPLC-DAD as a reference method

Electrodegradation of atrazine in water was performed using homemade (PA and PB) and purchased (PC) boron-doped diamond anodes. The degradation was monitored off-line by analyzing total organic carbon and high performance liquid chromatography with diode array detector (HPLC-DAD) and at-line by UV spectroscopy. The spectra were recorded every 2 min. The rank deficiency problem was resolved by assembling an augmented column-wise matrix. HPLC was employed to separate the original and byproducts degradation components. Aiming the same goal, multivariate curve resolution - alternating least squares (MCR-ALS) was applied to resolve the UV spectroscopic data. Comparison between HPLC and MCR-ALS separations is presented. By using MCR-ALS the spectra of atrazine and two byproducts were successfully resolved and the resulted concentration profiles properly represented the system studied. The ALS explained variance (R2) for PA, PB and PC was equal to 99.99% for all of them and the lack of fit for PA, PB and PC were 0.39, 0.34 and 0.54 respectively. The correlation (R) between the recovered and pure spectra were calculate for each electrodegradation, validating the MCR-ALS results. The average R was equal to 0.997. The spectral and concentration profiles described with this new approach are in agreement with HPLC-DAD results. The proposed method is an alternative to classical analyses for monitoring of the degradation process, mainly due to the simplicity, fast results and economy392137145COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE MINAS GERAIS - FAPEMIGnão temTEC-APQ-01307-12Os autores agradecem à FAPEMIG pelo financiamento do projeto (TEC-APQ-01307-12), à CAPES pela bolsa de estudos e à Companhia Brasileira de Metalurgia e Mineração (CBMM) por fornecer o nióbio usado como substrato para o BD