Navegando por Autor "Poppi, Ronei Jesus"
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Item Application of Kohonen neural network for evaluation of the contamination of Brazilian breast milk with polychlorinated biphenyls.(2013) Kowalski, Claudia Hoffmann; Silva, Gilmare Antônia da; Godoy, Helena Teixeira; Poppi, Ronei Jesus; Augusto, FábioDue 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, breastfeed- ing 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.Item Correlation of quantitative sensorial descriptors and chromatographic signals of beer using multivariate calibration strategies.(2012) Silva, Gilmare Antônia da; Maretto, Danilo Althmann; Bolini, Helena Maria André; Teófilo, Reinaldo Francisco; Augusto, Fábio; Poppi, Ronei JesusIn 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.Item Exploratory analysis of the volatile profile of beers by HS–SPME–GC.(2008) Silva, Gilmare Antônia da; Augusto, Fábio; Poppi, Ronei JesusKohonen Neural Network maps were used for exploratory analysis of Brazilian Pilsner beers. The input data consisted of the peak areas of the volatile profile compounds of samples obtained after headspace solid phase microextraction coupled to gas chromatography. The chromatographic peaks were identified as originating from compounds such as alcohols, esters, organic acids, phenolic compounds, ketone and others typically found in the headspace of such samples. Analysis of the Kohonen maps showed that the 20 different brands of beer could be grouped into six sets, with three of these sets having only one sample, according to the composition of their volatile fractions. The volatile species associated with the similarities and differences between each sample group were tentatively identified by mass spectrometry mand their contributions to the grouping are discussed.Item Isolation and quantification of dialkylmercury species by headspace solid phase microextraction and gas chromatography with atomic emission detection.(2008) Oliveira, Ana Maria de; Silva, Gilmare Antônia da; Poppi, Ronei Jesus; Augusto, FábioFoi desenvolvida uma metodologia para quantificar compostos dialquilmercúricos usando Microextração em Fase Sólida em Headspace (HS-SPME) e Cromatografia Gasosa com Detecção por Emissão Atômica (GC-AED). Os parâmetros para detecção de Hg foram otimizados usando planejamento fatorial e superfícies de resposta. Experimentos univariados foram empregados para determinar as condições de HS-SPME; as melhores fibras foram 75 m de Carboxen / PDMS e 65 m de PDMS / DVB. Porém, as primeiras foram descartadas pela extensa degradação térmica dos analitos na dessorção. O procedimento otimizado permite detectar os analitos em amostras aquosas com limite de detecção de 1,7 e 0,2 ng L-1 para dimetil- and dietilmercúrio, respectivamente. As curvas analíticas são lineares nas faixas de 36 a 180 ng L-1 (Me2Hg) e 38 a 190 ng L-1 (Et2Hg), com limite de quantificação de 38 ng L-1 (Me2Hg) e 29 ng L-1 (Et2Hg) e coeficientes de correlação de 0,998 para Me2Hg e 0,999 para Et2Hg.Item Simultaneous optimization by neuro-genetic approach for analysis of plant materials by laser induced breakdown spectroscopy.(2009) Nunes, Lidiane Cristina; Silva, Gilmare Antônia da; Trevizan, Lilian Cristina; Santos Júnior, Dario; Poppi, Ronei Jesus; Krug, Francisco JoséA simultaneous optimization strategy based on a neuro-genetic approach is proposed for selection of laser induced breakdown spectroscopy operational conditions for the simultaneous determination of macronutrients (Ca, Mg and P), micro-nutrients (B, Cu, Fe, Mn and Zn), Al and Si in plant samples. A laser induced breakdown spectroscopy system equipped with a 10 Hz Q-switched Nd:YAG laser (12 ns, 532 nm, 140 mJ) and an Echelle spectrometer with intensified coupled-charge device was used. Integration time gate, delay time, amplification gain and number of pulses were optimized. Pellets of spinach leaves (NIST 1570a) were employed as laboratory samples. In order to find a model that could correlate laser induced breakdown spectroscopy operational conditions with compromised high peak areas of all elements simultaneously, a Bayesian Regularized Artificial Neural Network approach was employed. Subsequently, a genetic algorithm was applied to find optimal conditions for the neural network model, in an approach called neuro-genetic. A single laser induced breakdown spectroscopy working condition that maximizes peak areas of all elements simultaneously, was obtained with the following optimized parameters: 9.0 μs integration time gate, 1.1 μs delay time, 225 (a.u.) amplification gain and 30 accumulated laser pulses. The proposed approach is a useful and a suitable tool for the optimization process of such a complex analytical problem.