Use of Artificial Neural Networks to predict Aqueous Two- Phases System Optimal Conditions on Bromelain’s Purification
Coelho, D.F.
Silva, C.A.
Machado, C.S.
Silveira, E.C.
Tambourgi, E.B.
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Coelho D., Silva C., Machado C., Silveira E., Tambourgi E., 2015, Use of Artificial Neural Networks to predict Aqueous Two- Phases System Optimal Conditions on Bromelain’s Purification, Chemical Engineering Transactions, 43, 1417-1422.
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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 far.
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