Partition coefficient prediction of Baker's yeast invertase in aqueous two phase systems using hybrid group method data handling neural network  

作　　者：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 

机构地区：[1]Laboratory of Biochemical Engineering,Chemical Engineering Department,Federal University of Rio Grande do Norte (UFRN)

出　　处：《Chinese Journal of Chemical Engineering》2017年第5期652-657,共6页中国化学工程学报（英文版）

基　　金：CAPES and Brazilian National Council of Research (CNPq) (Grant 407684/2013-1) for the financial support

摘　　要：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(1500–6000 Da), p H(4.0–7.0), percentage of PEG(10.0–20.0 w/w), percentage of MgSO_4(8.0–16.0 w/w), percentage of the cell homogenate(10.0–20.0 w/w) and the percentage of MnSO_4(0–5.0 w/w) added as cosolute. 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(AARD = 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.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（1500–6000 Da）, p H（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 cosolute. 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（AARD = 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.

关 键 词：Partitioning Invertase Aqueous Two Phase System GMDH Neural network 

分 类 号：TQ925[轻工技术与工程—发酵工程]