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One of the most important methods to produce porcine interferon α is   microbial fermentation. In the present study, recombinant Pichia pastoriswas   used. Broth’s antiviral activity is the key index of the expression level of   porcine interferon α. Measurement of antiviral activity is a time-consuming   and difficult task, which makes the research and production work inconvenient   and uncertain. To solve this problem, multivariable regression and artificial   neural network were applied to predict the antiviral activity based on five   on-line variables (induction time, temperature, dissolve doxygen, O2uptake   rate and CO2 evolution rate) and two off-line variables (methanol consumption   rate and total protein concentration).Parameters of the multivariable   quadratic polynomial regression equation were estimate dusing least square   methods. Optimization of artificial neural network(ANN)was achieved by   back-propagation and genetic algorithm. Verified by test set, the   ANN optimized by genetic algorithm had the best predictive performance and   generalization. The sensitivity analysis showed that CO2evolution rate,   O2 uptake rate and methanol consumption rate were the most relevant factors   for model’s output, except for the antiviral activity’s own previous value

chemical industry and chemical engineering quarterly

Prediction of porcine interferon α antiviral activity in fermentation by Pichia pastoris based on multivariable regression and artificial neural network