Advanced monitoring of viral amplification process by soft sensing

Abstract

In this study, a software sensor monitoring a viral amplification process is developed and validated. First, a dynamic model structure is proposed, describing Vero cell growth as well as the impact of viral infection, in accordance with the considered industrial application. A parameter identification procedure is set up based on a nonlinear least-square optimization criterion using several data sets provided by Sanofi Pasteur (Lyon, France). Second, an extended Kalman filter is designed considering a specific measurement configuration including a Raman probe sensing biomass, glucose, lactate and glutamine concentrations, and the estimation of exogenous variables such as the cell growth rate and viral amplification parameters. The obtained results validate the possibility to consider the EKF software sensor as a useful tool to monitor and report on viral amplification dynamics.