Application of <i>In-Situ</i> and Soft-Sensors for Estimation of Recombinant <i>P. pastoris</i> GS115 Biomass Concentration: A Case Analysis of HBcAg (Mut⁺) and HBsAg (Mut^S) Production Processes under Varying Conditions

• Main Authors: Oskars Grigs, Emils Bolmanis, Vytautas Galvanauskas
• Format: Article
• Language: English
• Published: MDPI AG 2021-02-01
• Series: Sensors
• Subjects: biomass concentration; <i>in-situ</i> and soft-sensors; turbidity; permittivity; signal filtering; off-gas analysis
• Online Access: https://www.mdpi.com/1424-8220/21/4/1268

Microbial biomass concentration is a key bioprocess parameter, estimated using various labor, operator and process cross-sensitive techniques, analyzed in a broad context and therefore the subject of correct interpretation. In this paper, the authors present the results of <i>P. pastoris </i>cell density estimation based on off-line (optical density, wet/dry cell weight concentration), <i>in-situ</i> (turbidity, permittivity), and soft-sensor (off-gas O₂/CO₂, alkali consumption) techniques. Cultivations were performed in a 5 L oxygen-enriched stirred tank bioreactor. The experimental plan determined varying aeration rates/levels, glycerol or methanol substrates, residual methanol levels, and temperature. In total, results from 13 up to 150 g (dry cell weight)/L cultivation runs were analyzed. Linear and exponential correlation models were identified for the turbidity sensor signal and dry cell weight concentration (DCW). Evaluated linear correlation between permittivity and DCW in the glycerol consumption phase (<60 g/L) and medium (for Mut⁺ strain) to significant (for Mut^S strain) linearity decline for methanol consumption phase. DCW and permittivity-based biomass estimates used for soft-sensor parameters identification. Dataset consisting from 4 Mut⁺ strain cultivation experiments used for estimation quality (expressed in NRMSE) comparison for turbidity-based (8%), permittivity-based (11%), O₂ uptake-based (10%), CO₂ production-based (13%), and alkali consumption-based (8%) biomass estimates. Additionally, the authors present a novel solution (algorithm) for uncommon <i>in-situ</i> turbidity and permittivity sensor signal shift (caused by the intensive stirrer rate change and antifoam agent addition) on-line identification and minimization. The sensor signal filtering method leads to about 5-fold and 2-fold minimized biomass estimate drifts for turbidity- and permittivity-based biomass estimates, respectively.