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Tambourgi E. B. - One of the best experts on this subject based on the ideXlab platform.
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Use of artificial neural networks to predict aqueous two- phases system optimal conditions on bromelain’s purification
Itália, 2020Co-Authors: Coelho D. F., Silva C. A., Machado C. S., Silveira E. C., Tambourgi E. B.Abstract:Bromelain is the denomination chosen to the group of endoproteases obtained from pineapple and from most of plants belonging to Bromeliaceae family. These enzymes have being widely studied in researches across the world due its physiological Activity and biotechnological potential. While Brazil still cultivating over 60,000 hectares of pineapple, there is a optimistic trend that aim bromelain's recovery from agriculture residues (stalk and leaves) and fruit processing residues (stem and bark) leading to a fully integrated process which aggregate value to vegetal residues. Our previous studies applied Aqueous Two-Phases Systems and Fractional Precipitation to purify bromelain and achieve purification factor and yield of 11.80 and 87.36, respectively. However, such studies were designed and analysed using Design Of Experiments (DOEs), which lead to an optimal condition but cannot predict with accuracy the complex phenomena of Partitioning using ATPS. This work is part of an initiative that aims establish a protocol to calculate more accurate Partitioning data through the use of Artificial Neural Networks (ANNs) over a dataset that has being improved continuously. The ANN will determine the relationship between five input parameters (temperature, PEG's molar mass, concentration of PEG, concentration of ammonium sulphate and dilution factor of sample) with three output parameters (protein Partition coefficient, Activity Partition coefficient and purification factor). The method applied a feed-forward neural network trained with Levenberg-Marquardt algorithm and the Bayesian regularization over the normalized experimental data. The network generated proved the reliability of the method which combined datasets from different DOEs and obtained regression coefficient (~.99) and error (MSE ~0.02) satisfactory for such amount of data used so far4314171422CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPsem informaçã
Elias Basile - One of the best experts on this subject based on the ideXlab platform.
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Use Of Artificial Neural Networks To Predict Aqueous Two-phases System Optimal Conditions On Bromelain's Purification
MILANO, 2016Co-Authors: Diego De Freitas, Camila Alves, Camila Sacconi, Elias BasileAbstract:Bromelain is the denomination chosen to the group of endoproteases obtained from pineapple and from most of plants belonging to Bromeliaceae family. These enzymes have being widely studied in researches across the world due its physiological Activity and biotechnological potential. While Brazil still cultivating over 60,000 hectares of pineapple, there is a optimistic trend that aim bromelain's recovery from agriculture residues (stalk and leaves) and fruit processing residues (stem and bark) leading to a fully integrated process which aggregate value to vegetal residues. Our previous studies applied Aqueous Two-Phases Systems and Fractional Precipitation to purify bromelain and achieve purification factor and yield of 11.80 and 87.36, respectively. However, such studies were designed and analysed using Design Of Experiments (DOEs), which lead to an optimal condition but cannot predict with accuracy the complex phenomena of Partitioning using ATPS. This work is part of an initiative that aims establish a protocol to calculate more accurate Partitioning data through the use of Artificial Neural Networks (ANNs) over a dataset that has being improved continuously. The ANN will determine the relationship between five input parameters (temperature, PEG's molar mass, concentration of PEG, concentration of ammonium sulphate and dilution factor of sample) with three output parameters (protein Partition coefficient, Activity Partition coefficient and purification factor). The method applied a feed-forward neural network trained with Levenberg-Marquardt algorithm and the Bayesian regularization over the normalized experimental data. The network generated proved the reliability of the method which combined datasets from different DOEs and obtained regression coefficient (similar to.99) and error (MSE similar to 0.02) satisfactory for such amount of data used so far.431417142
Adalberto Pessoa - One of the best experts on this subject based on the ideXlab platform.
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bromelain Partitioning in two phase aqueous systems containing peo ppo peo block copolymers
Journal of Chromatography B, 2004Co-Authors: Ana Paula Brescancini Rabelo, Elias Basile Tambourgi, Adalberto PessoaAbstract:Abstract Bromelain is an enzymatic complex obtained from pineapple (Ananas comosus) fruits and stem. Thermoseparation of bromelain by poly(ethylene oxide) (PEO)– poly(propylene oxide) (PPO)– poly(ethylene oxide) (PEO) block copolymers aqueous solutions was studied. Triblock copolymers with different EO percentages and different molecular mass were evaluated. Copolymer solutions at different pH values, buffer concentrations and copolymer concentrations were investigated. It was found that cloud point temperature increases as a function of %EO and decreases with copolymer molecular mass, copolymer concentration and buffer concentration. The results showed that all the studied factors influenced enzyme Partition. The best conditions were copolymer with 10% EO and molecular mass of 2000 g/mol, temperature of 25 °C, copolymer concentration of 5% (w/w), pH 6.0 and salt concentration of 15 mM. Enzyme Activity recovery around 79.5%, purification factor around 1.25 and Activity Partition coefficient around 1.4 were obtained.
Pessoa Jr. A. - One of the best experts on this subject based on the ideXlab platform.
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Bromelain Partitioning In Two-phase Aqueous Systems Containing Peo-ppo-peo Block Copolymers
2015Co-Authors: Rabelo A.p.b., Tambourgi E.b., Pessoa Jr. A.Abstract:Bromelain is an enzymatic complex obtained from pineapple (Ananas comosus) fruits and stem. Thermoseparation of bromelain by poly(ethylene oxide) (PEO)- poly(propylene oxide) (PPO)- poly(ethylene oxide) (PEO) block copolymers aqueous solutions was studied. Triblock copolymers with different EO percentages and different molecular mass were evaluated. Copolymer solutions at different pH values, buffer concentrations and copolymer concentrations were investigated. It was found that cloud point temperature increases as a function of %EO and decreases with copolymer molecular mass, copolymer concentration and buffer concentration. The results showed that all the studied factors influenced enzyme Partition. The best conditions were copolymer with 10% EO and molecular mass of 2000 g/mol, temperature of 25°C, copolymer concentration of 5% (w/w), pH 6.0 and salt concentration of 15 mM. Enzyme Activity recovery around 79.5%, purification factor around 1.25 and Activity Partition coefficient around 1.4 were obtained. © 2004 Elsevier B.V. All rights reserved.80716168Brooks, D.E., Walter, H., Fisher, D., (1985) Partitioning in Aqueous Two Phase Systems, , Academic Press, OrlandoAlbertsson, P.A., (1986) Partition of Cell Particles and Macromolecules, Third Ed., 323p. , Wiley/Interscience, New YorkWalter, H., Johansson, G., (1994) Methods in Enzymology, 228, pp. 600-608. , Academic Press, LondresAlred, P.A., Tjerneld, F., Koslowski, A., Harris, J.M., (1992) Bioseparation, 2, p. 363Franco, T.T., Galaev, Y.U., Hatti-Kaul, R., Holberg, N., Bülow, L., Mattiasson, B., (1997) Biotechnol. Tech., 11, p. 231Persson, J., Johansson, H.-O., Tjerneld, F., (1999) J. Chromatogr. a, 864, p. 31Persson, J., Nystrom, L., Ageland, H., Tjerneld, F., (1999) J. Chem. Technol. Biotechnol., 74, p. 238Alexandridis, P., Hatton, T.A., (1995) Colloids Surf. A: Physicochem. Eng. Aspects, 96, p. 1Hvidt, S., (1995) Colloids Surf. A: Physicochem. Eng. Aspects, 112, p. 201Persson, J., Kaul, A., Tjerneld, F., (2000) J. Chromatogr. B, 743, p. 115Berggren, K., Johansson, H.-O., Tjerneld, F., (1995) J. Chromatogr. a, 718, p. 67Saitoh, T., Tani, H., Kamidate, T., Watanabe, H., (1995) Trends Anal. Chem., 14, p. 213Cunha, M.T., Tjerneld, F., Cabral, J.M.S., Aires-Barros, M.R., (1998) J. Chromatogr. B, 711, p. 53Feinstein, G., Whitaker, J.R., (1964) Biochemistry, 3, p. 1050Murachi, T., Bromelain enzymes (1976) Methods in Enzymology, 45, pp. 475-485. , L. Lorand (Ed.), Academic Press, New YorkArroyo-Reyna, A., Hernández-Arana, A., (1995) Biochim. Biophys. Acta, 1248, p. 123Bradford, M.M., (1976) Anal. Biochem., 72, p. 248A.C.W. Cesar, R. Silva, A.C. Lucarini, RIC, São Carlos, SP, Brazil 01 (1999) 47-54Baldini, V.L.S., Iaderoza, M., Ferreira, E.A.H., Sales, A.M., Draetta, I.S., Giacomelli, E.J., (1993) Colet. ITAL, Campinas, 23, p. 44Karlstron, G., (1985) J. Phys. Chem., 89, p. 4962Alred, P.A., Koslowski, A., Harris, J.M., Tjerneld, F., (1994) J. Chromatogr. a, 659, p. 28
Coelho D. F. - One of the best experts on this subject based on the ideXlab platform.
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Use of artificial neural networks to predict aqueous two- phases system optimal conditions on bromelain’s purification
Itália, 2020Co-Authors: Coelho D. F., Silva C. A., Machado C. S., Silveira E. C., Tambourgi E. B.Abstract:Bromelain is the denomination chosen to the group of endoproteases obtained from pineapple and from most of plants belonging to Bromeliaceae family. These enzymes have being widely studied in researches across the world due its physiological Activity and biotechnological potential. While Brazil still cultivating over 60,000 hectares of pineapple, there is a optimistic trend that aim bromelain's recovery from agriculture residues (stalk and leaves) and fruit processing residues (stem and bark) leading to a fully integrated process which aggregate value to vegetal residues. Our previous studies applied Aqueous Two-Phases Systems and Fractional Precipitation to purify bromelain and achieve purification factor and yield of 11.80 and 87.36, respectively. However, such studies were designed and analysed using Design Of Experiments (DOEs), which lead to an optimal condition but cannot predict with accuracy the complex phenomena of Partitioning using ATPS. This work is part of an initiative that aims establish a protocol to calculate more accurate Partitioning data through the use of Artificial Neural Networks (ANNs) over a dataset that has being improved continuously. The ANN will determine the relationship between five input parameters (temperature, PEG's molar mass, concentration of PEG, concentration of ammonium sulphate and dilution factor of sample) with three output parameters (protein Partition coefficient, Activity Partition coefficient and purification factor). The method applied a feed-forward neural network trained with Levenberg-Marquardt algorithm and the Bayesian regularization over the normalized experimental data. The network generated proved the reliability of the method which combined datasets from different DOEs and obtained regression coefficient (~.99) and error (MSE ~0.02) satisfactory for such amount of data used so far4314171422CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPsem informaçã
