Accurate and cost‐effective prediction of HBsAg titer in industrial scale fermentation process of recombinant Pichia pastoris by using neural network based soft sensor

In the current work, the attempt was made to apply best‐fitted artificial neural network (ANN) architecture and the respective training process for predicting final titer of hepatitis B surface antigen (HBsAg), produced intracellularly by recombinant Pichia pastoris Mut+ in the commercial scale. For...

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Vydáno v:	Biotechnology and applied biochemistry Ročník 66; číslo 4; s. 681 - 689
Hlavní autoři:	Hosseini, Seyed Nezamedin, Javidanbardan, Amin, Khatami, Maryam
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Wiley Subscription Services, Inc 01.07.2019
Témata:	Algorithms ANN Antigens Artificial neural networks Back propagation Bayesian analysis Biomass Biopharmaceuticals Bioreactors - economics Datasets Fermentation Growth rate HBsAg Hepatitis B Hepatitis B surface antigen Hepatitis B Surface Antigens - chemistry Hepatitis B Surface Antigens - economics Hepatitis B Surface Antigens - metabolism large‐scale fermentation Mathematical analysis Mut+ Pichia pastoris Neural networks Neural Networks, Computer parameter estimation Pichia - chemistry Pichia - metabolism Pichia pastoris Predictions Recombinant Proteins - chemistry Recombinant Proteins - economics Recombinant Proteins - metabolism Regression analysis Regularization soft sensor titer Yeast ANN Mut+ Pichia pastoris soft sensor titer HBsAg parameter estimation large-scale fermentation
ISSN:	0885-4513, 1470-8744, 1470-8744
On-line přístup:	Získat plný text
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