Recuperação e purificação de quitosanases usando adsorção em leito expandido com streamline DEAE com modelagem e simulação usando redes neurais

Expanded Bed Adsorption (EBA) is an integrative process that combines concepts of chromatography and fluidization of solids. The many parameters involved and their synergistic effects complicate the optimization of the process. Fortunately, some mathematical tools have been developed in order to guide the investigation of the EBA system. In this work the application of experimental design, phenomenological modeling and artificial neural networks (ANN) in understanding chitosanases adsorption on ion exchange resin Streamline® DEAE have been investigated. The strain Paenibacillus ehimensis NRRL B-23118 was used for chitosanase production. EBA experiments were carried out using a column of 2.6 cm inner diameter with 30.0 cm in height that was coupled to a peristaltic pump. At the bottom of the column there was a distributor of glass beads having a height of 3.0 cm. Assays for residence time distribution (RTD) revelead a high degree of mixing, however, the Richardson-Zaki coefficients showed that the column was on the threshold of stability. Isotherm models fitted the adsorption equilibrium data in the presence of lyotropic salts. The results of experiment design indicated that the ionic strength and superficial velocity are important to the recovery and purity of chitosanases. The molecular mass of the two chitosanases were approximately 23 kDa and 52 kDa as estimated by SDS-PAGE. The phenomenological modeling was aimed to describe the operations in batch and column chromatography. The simulations were performed in Microsoft Visual Studio. The kinetic rate constant model set to kinetic curves efficiently under conditions of initial enzyme activity 0.232, 0.142 e 0.079 UA/mL. The simulated breakthrough curves showed some differences with experimental data, especially regarding the slope. Sensitivity tests of the model on the surface velocity, axial dispersion and initial concentration showed agreement with the literature. The neural network was constructed in MATLAB and Neural Network Toolbox. The cross-validation was used to improve the ability of generalization. The parameters of ANN were improved to obtain the settings 6-6 (enzyme activity) and 9-6 (total protein), as well as tansig transfer function and Levenberg-Marquardt training algorithm. The neural Carlos Eduardo de Araújo Padilha dezembro/2013 9 networks simulations, including all the steps of cycle, showed good agreement with experimental data, with a correlation coefficient of approximately 0.974. The effects of input variables on profiles of the stages of loading, washing and elution were consistent with the literature

Recuperação e purificação de proteínas do soro de queijo tipo coalho usando cromatografia de troca iônica e interação hidrofóbica em leito na forma expandida

Expanded Bed Adsorption plays an important role in the downstream processing mainly for reducing costs as well as steps besides could handling cells homogenates or fermentation broth. In this work Expanded Bed Adsorption was used to recover and purify whey proteins from coalho cheese manufacture using Streamline DEAE and Streamline SP both ionic resins as well as a hydrophobic resin Streamline Phenyl. A column of 2.6 cm inner diameter with 30 cm in height was coupled to a peristaltic pump. Hydrodynamics study was carried out with the three resins using Tris-HCl buffer in concentration of 30, 50 and 70 mM, with pH ranging from 7.0 to 8.0. In this case, assays of the expansion degree as well as Residence Time Distribution (RTD) were carried out. For the recovery and purification steps, a whey sample of 200 mL, was submitted to a column with 25mL of resin previously equilibrated with Tris/HCl (50 mM, pH 7.0) using a expanded bed. After washing, elution was carried out according the technique used. For ionic adsorption elution was carried out using 100 mL of Tris/HCl (50 mM, pH 7.0 in 1M NaCl). For Hydrophobyc interaction elution was carried out using Tris/HCl (50 mM, pH 7.0). Adsorption runs were carried out using the three resins as well as theirs combination. Results showed that for hydrodynamics studies a linear fit was observed for the three resins with a correlation coefficient (R2) about 0.9. In this case, Streamline Phenyl showed highest expansion degree reaching an expansion degree (H0/H) of 2.2. Bed porosity was of 0.7 when both resins Streamline DEAE and Streamline SP were used with StremLine Phenyl showing the highest bed porosity about 0.75. The number of theorical plates were 109, 41.5 and 17.8 and the axial dipersion coefficient (Daxial) were 0.5, 1.4 and 3.7 x 10-6 m2/s, for Streamline DEAE, Streamline SP and Streamline Phenyl, respectively. Whey proteins were adsorved fastly for the three resins with equilibrium reached in 10 minutes. Breakthrough curves showed that most of proteins stays in flowthrough as well as washing steps with 84, 77 and 96%, for Streamline DEAE, Streamline SP and Streamline Phenyl, respectively. It was observed protein peaks during elution for the three resins used. According to these peaks were identified 6 protein bands that could probably be albumin (69 KDa), lactoferrin (76 KDa), lactoperoxidase (89 KDa), β-lactoglobulin (18,3 KDa) e α-lactoalbumin (14 KDa), as well as the dimer of beta-lactoglobulin. The combined system compound for the elution of Streamline DEAE applied to the Streamline SP showed the best purification of whey proteins, mainly of the α-lactoalbumina

Análise da variabilidade climática sobre a produtividade da soja no município de Bom Jesus (PI)

The climate is still main responsible for the variations soybean productivity (Glycine max (L.) Merrill), exerting a limiting action on these agricultural systems. The bomjesuense cerrado, this culture has proved, over the years, an increase of cultivated areas, however, productivity does not keep the same pace, going through periods of oscillations. Thus, although the crop is added to high technology, culture has great vulnerability to climatic adversities. Thus, the present study aims to analyze possible trends in meteorological variables, which can influence the soybean yield in Bom Jesus. For this purpose, different datasets were used, as follows: i) two periods of daily data (1984-2014 and 1974-2014), both obtained from the National Meteorological Institute (INMET); ii) climate normals from 1961-1990 as defined by INMET; iii) local agricultural production data of soybean-year (1997/1998 to 2012/2013) obtained from the Municipal Agricultural Production (PAM) dataset, which is management by Brazilian Institute of Geography and Statistics (IBGE). The analysis procedures included calculations of climate normals for 1984 to 2014 period and some statistical applications, as follows: i) the Wilcoxon test, used to evaluate differences between climate normals (1961 to 1990 and 1984 to 2014); ii) the Mann-Kendall nonparametric test, in order to analyze the linear trend of agrometeorological variables (rainfall, maximum temperature, minimum temperature and diurnal range of temperature; iii) cluster analysis by Ward method and the Spearman correlation test (rs) to identify the relationship between agrometeorological variable and soybean annual productivity. We adopted a statistical significance level of 5%. The results indicate changes in seasonality of the 1984-2014 climatology with respect to past climatology for all variables analyzed, except for insolation and precipitation. However, the monthly analysis of precipitation indicate negative trend during October and positive trend in December, causing a delay in start of rainy season. If this trend is persistent this result must be considered in futures definitions of the soybean crop sowing date over the region studied. With Mann-Kendall test was possible to identify positive trends with statistical significance in maximum temperature for all month forming part of soybean cycle (from November to April), which in turn tends to cause adverse effects on crop physiology, and consequently impacts on the final yield. Was identified a significant positive correlation between soybean yield and precipitation observed in March, thus precipitation deficit in this month is harmful to the soybean crop development. No statistically significant correlation was identified among maximum temperature, minimum temperature, and DTR with annual soybean productivity due these range of meteorological variables are not limiting factors in the final soybean yield in Bom Jesus (PI). It is expected that this study will contribute to propose planning strategies considering the role of climate variability on soybean crop final yield.