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Response surface methodology and artificial neural network modelling of an aqueous two-phase system for purification of a recombinant alkaline active xylanase

Paper ID Volume ID Publish Year Pages File Format Full-Text
34172 45007 2016 11 PDF Available
Title
Response surface methodology and artificial neural network modelling of an aqueous two-phase system for purification of a recombinant alkaline active xylanase
Abstract

•Describes purification of a recombinant xylanase using aqueous two-phase systems.•Two stages aqueous two phase system were used.•RSM and ANN models were used to achieve a higher yield and purity of the enzyme.•Both ANN and RSM models provided similar predictions, ANN showing more accuracy.•The ATPS was scaled-up to investigate its ability to operate at a larger scale.

A two-stage polyethylene glycol (PEG)-phosphate aqueous two-phase system was used for purification of a highly thermostable and alkaline active recombinant xylanase. Response surface methodology (RSM) and artificial neural network (ANN) have been used to develop predictive models for simulation and optimization of purification process. Effects of pH, PEG molecular weight, concentrations of phosphate, PEG and NaCl on the partitioning of the target enzyme and the contaminants were studied using a central composite design of experiments. The best first stage purification was achieved using 6% PEG 6000, 20% phosphate and pH 6. The optimum back extraction stage system consist of 10% phosphate, 10% NaCl, pH 10 and the first stage separation top phase. After the two stage phase separations, about 78% of the original enzyme activity was recovered and the specific activity of the purified enzyme was increased by a factor of 6.7. Also, the aqueous two-phase system was scaled-up 100 times. After back-extraction, the specific activity increased 6.56 times with 72% total yield. A similar design was also used to obtain a training set for ANN. A comparison between the model results and experimental data gave high correlation coefficient (R2) and showed that both models were able to predict the partitioning behavior. The results demonstrated a higher prediction accuracy of ANN compared to RSM. This superiority of ANN over other multi factorial approaches could make this estimation technique a very helpful tool for purification process.

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Keywords
Aqueous two-phase; Purification; Downstream processing; Central composition design; Artificial neural network; Enzymes
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Response surface methodology and artificial neural network modelling of an aqueous two-phase system for purification of a recombinant alkaline active xylanase
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Publisher
Database: Elsevier - ScienceDirect
Journal: Process Biochemistry - Volume 51, Issue 3, March 2016, Pages 452–462
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us