Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4764065 | Chinese Journal of Chemical Engineering | 2017 | 22 Pages |
Abstract
A hybrid GMDH neural network model has been developed in order to predict the partition coefficients of invertase from Baker's yeast. ATPS experiments were carried out changing the molar average mass of PEG (1500-6000Â Da), pH (4.0-7.0), percentage of PEG (10.0-20.0 w/w), percentage of MgSO4 (8.0-16.0 w/w), percentage of the cell homogenate (10.0-20.0 w/w) and the percentage of MnSO4 (0-5.0 w/w) added as co-solute. The network evaluation was carried out comparing the partition coefficients obtained from the hybrid GMDH neural network with the experimental data using different statistical metrics. The hybrid GMDH neural network model showed better fitting (AARDÂ =Â 32.752%) as well as good generalization capacity of the partition coefficients of the ATPS than the original GMDH network approach and a BPANN model. Therefore hybrid GMDH neural network model appears as a powerful tool for predicting partition coefficients during downstream processing of biomolecules.
Related Topics
Physical Sciences and Engineering
Chemical Engineering
Chemical Engineering (General)
Authors
Carlos Eduardo de Araújo Padilha, Sérgio Dantas de Oliveira Júnior, Domingos Fabiano de Santana Souza, Jackson Araújo de Oliveira, Gorete Ribeiro de Macedo, Everaldo Silvino dos Santos,