Gilmare Antônia da Silva

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 panellists.

Manganese ore tailing : optimization of acid leaching conditions and recovery of soluble manganese.

Manganese recovery from industrial ore processing waste by means of leaching with sulfuric acid was the objective of this study. Experimental conditions were optimized by multivariate experimental design approaches. In order to study the factors affecting leaching, a screening step was used involving a full factorial design with central point for three variables in two levels (2(3)). The three variables studied were leaching time, concentration of sulfuric acid and sample amount. The three factors screened were shown to be relevant and therefore a Doehlert design was applied to determine the best working conditions for leaching and to build the response surface. By applying the best leaching conditions, the concentrations of 12.80 and 13.64 %w/w of manganese for the global sample and for the fraction -44 + 37 μm, respectively, were found. Microbeads of chitosan were tested for removal of leachate acidity and recovering of soluble manganese. Manganese recovery from the leachate was 95.4%. Upon drying the leachate, a solid containing mostly manganese sulfate was obtained, showing that the proposed optimized method is efficient for manganese recovery from ore tailings.

Application of Kohonen neural network for evaluation of the contamination of Brazilian breast milk with polychlorinated biphenyls

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.

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).

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 agua foram coletadas em 10 pontos em trechos do leste do Quadrilatero Ferrifero (QF), que e uma regiao mineira situada no sudeste do Brasil. Os objetivos deste estudo foram encontrar possiveis relacoes entre carbono orgânico dissolvido (COD), metais e outros parâmetros fisico-quimicos medidos utilizando a rede neural de Kohonen como ferramenta para analisar esses dados geoquimicos multivariados na area estudada. As analises fisico-quimicas foram feitas in situ e em laboratorio, onde as concentracoes de COD e varios ions metalicos foram determinadas. A rede de Kohonen permitiu a visualizacao e interpretacao mais amigaveis dos dados, alem de definir relacoes entre eles. Assim, para os dados analisados, foi verificada relacao entre COD e Fe e um possivel efeito da sazonalidade na distribuicao das amostras. Possiveis evidencias litologicas puderam ser detectadas pela analise exploratoria, especialmente se considerados os elementos Ca, Mg, Mn e Sr. Water samples were collected at 10 points in parts of the eastern Quadrilatero Ferrifero (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.

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.

MCR-ALS aplicado no monitoramento quantitativo do processo de eletrodegradação da atrazina usando espectros UV : resultados comparativos com HPLC-DAD como um método de referência.

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 economy.