کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4764065 | 1423376 | 2017 | 22 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Partition coefficient prediction of Baker's yeast invertase in aqueous two phase systems using hybrid group method data handling neural network
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی شیمی
مهندسی شیمی (عمومی)
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Chinese Journal of Chemical Engineering - Volume 25, Issue 5, May 2017, Pages 652-657
Journal: Chinese Journal of Chemical Engineering - Volume 25, Issue 5, May 2017, Pages 652-657
نویسندگان
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,