RESUMEN
At the end of 2015, the Fundão dam belonging to the Samarco S.A. mining company was ruptured, releasing a flood of mud into the Gualaxo do Norte River, which advanced into the Doce River. The aim of the present study was to apply exploratory multivariate approaches to water quality data obtained during sampling campaigns at the Gualaxo do Norte River during the dry and rainy seasons, between July 2016 and June 2017. A total of 27 locations along the river were sampled, covering unaffected areas and regions influenced by the tailings waste from the dam. Determinations of chemical, physical, and microbiological water quality parameters were performed. Application of principal component analysis (PCA) resulted in the first two components together explaining 39.49% and 37.91% of the total variance for the dry and rainy season data, respectively. In both cases, the PCA groups were related to variables such as turbidity and total solids, which both presented higher values in regions affected by the mud flow. These results are in agreement with those obtained by the Kohonen neural network method, where two-dimensional maps confirmed the samples according to the affected and unaffected area by the disaster.
Asunto(s)
Contaminantes Químicos del Agua , Calidad del Agua , Ríos , Monitoreo del Ambiente , Brasil , Contaminantes Químicos del Agua/análisisRESUMEN
During this research, chemometric approaches were applied for optimization of the low-temperature partitioning extraction (LTPE) for the simultaneous analysis of the pesticides: acephate, difenoconazole, fenamidone, fluazifop, fluazinam, methamidophos, and thiamethoxam from surface water samples and determination by high-performance liquid chromatography-electrospray ionization-tandem mass spectrometry. It was used the 23 full factorial and the Doehlert experimental designs. The extraction technique was optimized by evaluating the effects of the three variables: sample pH, ionic strength (addition of Na2HPO4), and organic solvent volume. Considering the interest to find an optimal condition for all analytes simultaneously, the best extraction parameters found were as follows: pH = 5.33, concentration of Na2HPO4 = 0.0088 mol L-1 and organic phase volume = 4.5 mL. The optimized methodology showed LOD and LOQ levels from 0.33 to 8.13 ng L-1 and from 1.09 to 26.84 ng L-1, respectively. The recovery values ranged from 38.37 and 99.83% and the RSD values varied from 2.33 to 18.92%. The method was applied to surface water analysis sampled in areas with intensive agricultural practices in Ouro Branco City, Minas Gerais, Brazil. The difenoconazole was detected in concentrations between 12.53 and 94.76 ng L-1.
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Cromatografía Líquida de Alta Presión/métodos , Plaguicidas/análisis , Espectrometría de Masas en Tándem/métodos , Agua/análisis , Agricultura , Brasil , Compuestos Organotiofosforados , Plaguicidas/química , Fosforamidas , Proyectos de Investigación , TemperaturaRESUMEN
Humic substances (HS) are ubiquitous organic compounds able to affect mobility and availability of arsenic (As) in aquatic systems. Although it is known that associations between HS and As occur mainly via iron (Fe)-cationic bridges, the behaviour and distribution of this metalloid in HS- and Fe-rich environments is still not fully understood. In this paper, the quality of HS from different rivers in Brazil and Germany and its influence on the behaviour of As(V) under different Fe(III) concentrations were investigated. HS were extracted from four different rivers (Cascatinha, Holtemme, Selke and Warme Bode), characterised and fractionated into different molecular weight sizes (10, 5 and 1 kDa). Complexation tests were performed using an ultrafiltration system and 1 kDa membranes. All data was analysed using the Kohonen neural network (SOM - Self organising maps). All samples, except Selke, exhibited similar results of free As (<1 kDa). The results suggested that associations between HS, Fe and As were dependent on nitrogen (N)-aromatic carbon (C), amount of sulphur (S) and the molecular size of the HS. Although all HS appeared to be similar after looking at most variables analysed, the SOM could discriminate them into three different groups. Characterisation of the HS indicated that they had terrestrial material (from C3 plants) as precursor material. Most of the As and Fe was distributed in the fractions of higher (>10 kDa) and lower (<1 kDa) size. HS quality is an important factor to take into account when studying the behaviour of As in HS-rich environments.
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Arsénico/análisis , Compuestos Férricos/farmacología , Agua Dulce/análisis , Sustancias Húmicas/análisis , Brasil , Carbono/análisis , Agua Dulce/química , Alemania , Hierro/análisis , Nitrógeno/análisis , Tamaño de la Partícula , Azufre/análisis , Ultrafiltración/métodosRESUMEN
The distribution of metals and metalloids among particulate, dissolved, colloidal, free, and labile forms in natural waters is of great environmental concern since it determines their transportation behaviour and bioavailability. Organic matter can have an important role for this distribution process, since it is an important complexing agent and ubiquitous in the aquatic environment. We studied the distribution, mobility and bioavailability of Al, As and Fe in natural waters of a mining area (Quadrilátero Ferrífero, Brazil) and the influence of organic matter in these processes. Water samples were taken from 12 points during the dry and rainy seasons, filtrated at 0.45 µm and ultrafiltrated (<1 kDa) to separate the particulate, colloidal and free fractions. Diffusive gradients in thin films (DGT) were deployed at 5 sampling points to study the labile part of the elements. Total and dissolved organic carbon and the physicochemical parameters were measured along with the sampling. The results of ultrafiltration (UF) and DGT were compared. The relationship among the variables was studied through multivariate analysis (Kohonen neural network), which showed that the seasonality did not impact most of the samples. Fe and Al occurred mainly in the particulate fraction whereas As appeared more in the free fraction. Most of the dissolved Fe and Al were inert (colloidal form) while As was more labile and bioavailable. The results showed that sampling points with a higher quantity of complexed Fe (colloidal fraction) showed less labile As, which may indicate formation of ternary complexes among organic matter, As and Fe.
Asunto(s)
Arsénico/análisis , Monitoreo del Ambiente/métodos , Minería , Ríos/química , Contaminantes Químicos del Agua/análisis , Disponibilidad Biológica , Brasil , Metales/análisis , Lluvia , Estaciones del Año , Solubilidad , Ultrafiltración/métodos , Agua/análisisRESUMEN
Due to the tendency of polychlorinated biphenyls (PCB) to accumulate in matrixes with high lipid content, the contamination of the breast milk with these compounds is a serious issue, mainly to the newborn. In this study, milk samples were collected from breastfeeding mothers belonging to 4 Brazilian regions (south, southeast, northeast and north). Twelve PCB were analyzed by HS-SPME-GC-ECD and the corresponding peak areas were correlated to the answers to a questionnaire of general habits, breastfeeding and characteristics of the living places. To realize this exploratory analyze, self-organizing maps generated applying Kohonen neural network were applied. It was possible to verify the occurrence of different PCB congeners in the breast milk relating to the region of the Brazil that the breastfeeding lives, the proximity to an industry, the proximity to a contaminated river or sea, the type of milk (colostrum, foremilk and hindmilk) and the number of past pregnancies.
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Calostro/química , Contaminantes Ambientales/aislamiento & purificación , Leche Humana/química , Bifenilos Policlorados/aislamiento & purificación , Brasil , Lactancia Materna , Cromatografía de Gases/instrumentación , Cromatografía de Gases/métodos , Femenino , Número de Embarazos , Humanos , Recién Nacido , Redes Neurales de la Computación , Microextracción en Fase Sólida , Encuestas y Cuestionarios , Topografía MédicaRESUMEN
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 panellists.
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Cerveza/análisis , Cromatografía de Gases y Espectrometría de Masas , Microextracción en Fase Sólida , Adulto , Algoritmos , Calibración , Estudios de Evaluación como Asunto , Humanos , Persona de Mediana Edad , Análisis Multivariante , Reproducibilidad de los Resultados , GustoRESUMEN
Polychlorinated biphenyls (PCB) can eventually contaminate breast milk, which is a serious issue to the newborn due to their high vulnerability. Solid phase microextraction (SPME) can be a very convenient technique for their isolation and pre-concentration prior chromatographic analysis. Here, a simultaneous multioptimization strategy based on a neuro-genetic approach was applied to a headspace SPME method for determination of 12 PCB in human milk. Gas chromatography with electron capture detection (ECD) was adopted for the separation and detection of the analytes. Experiments according to a Doehlert design were carried out with varied extraction time and temperature, media ionic strength and concentration of the methanol (co-solvent). To find the best model that simultaneously correlate all PCB peak areas and SPME extraction conditions, a multivariate calibration method based on a Bayesian Neural Network (BNN) was applied. The net output from the neural network was used as input in a genetic algorithm (GA) optimization operation (neuro-genetic approach). The GA pointed out that the best values of the overall SPME operational conditions were the saturation of the media with NaCl, extraction temperature of 95 degrees C, extraction time of 60 min and addition of 5% (v/v) methanol to the media. These optimized parameters resulted in the decrease of the detection limits and increase on the sensitivity for all tested analytes, showing that the use of neuro-genetic approach can be a promising way for optimization of SPME methods.
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Técnicas de Química Analítica/métodos , Leche Humana/metabolismo , Neurología/métodos , Bifenilos Policlorados/análisis , Calibración , Cromatografía/métodos , Cromatografía de Gases , Técnicas Genéticas , Humanos , Iones , Análisis Multivariante , Redes Neurales de la Computación , Reproducibilidad de los Resultados , Solventes , TemperaturaRESUMEN
A simultaneous optimization strategy based on neuro-genetic approach has been applied to a HS-SPME-GC-ECD (Headspace Solid Phase Microextraction coupled to Gas Chromatography with Electron Capture Detection) method for simultaneous determination of the pesticides chlorotalonil, methyl parathion, malathion, alpha-endosulfan and beta-endosulfan in herbal infusions of Passiflora alata (Dryander). Two types of extractive fibers were used: a home-made device coated by sol-gel process with polydimethylsiloxane-poly(vinyl alcohol) (PDMS/PVA) and a commercial PDMS. The effects of extraction parameters such as dilution of the infusion, extraction temperature and time, as well as sample ionic strength were evaluated through the Doehlert design. To find a model that could relate these extraction parameters with the extraction efficiency of all pesticide simultaneously, a Bayesian Regularized Artificial Neural Network (BRANN) approach was employed. Subsequently, Genetic Algorithm (GA) was applied to attain the optimum values from the model developed by the neural network. The use of the proposed approach allowed the determination of a single extraction condition that maximized the peak areas of all pesticides simultaneously, showing a promising and a suitable new procedure to the optimization process of complex analytical problems.