Two-Stage Fault Diagnosis of Pichia Pastoris Fermentation Based on an Auto-Associative Neural Network

doi:10.3969/j.issn.1673-1689.2012.06.006

Home > Archive>Volume 31, Issue 6, 2012 >592-598. DOI:10.3969/j.issn.1673-1689.2012.06.006

Two-Stage Fault Diagnosis of Pichia Pastoris Fermentation Based on an Auto-Associative Neural Network
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                        10.3969/j.issn.1673-1689.2012.06.006
                    
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Abstract:

For the IL-2-HSA expression with Pichia pastoris,methanol concentration and pH during induction phase are two important parameters affecting heterologous protein production and should be strictly controlled at adequate levels.In this study,based on the effective recognition of physiological status and characteristics of parameters,an auto-associative neural network (AANN) model was used for two-stage fault diagnosis in Pichia pastoris fermentation processes.The optimized AANN could provide on-line and accurate fault alarm for Pichia pastoris induction stage.It was potentially helpful in supplying useful information for removing fault and recovering abnormal fermentation.When detecting methanol over-feeding,glycerol limited feeding could improve the cell activity and release the toxicity of methanol.

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MIN-Jie Gao, XIAO-Bei Zhan, ZHI-Yong Zheng, JIAN-Rong Wu, HU Jin. Two-Stage Fault Diagnosis of Pichia Pastoris Fermentation Based on an Auto-Associative Neural Network[J]. Journal of Food Science and Biotechnology,2012,31(6):592-598.

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Online: June 17,2014
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