Biomass estimation in Pichia pastoris cultures by combined single-wavelength fluorescence measurements

In this work, the evolution of different biogenic fluorophores involved in the metabolism of Pichia pastoris was determined at four different single-wavelength pairs (excitation/emission) during batch culture in microwell plates and used for effective and reliable biomass estimation by means of chemometric tools. The chemometric tools for biomass estimation were multiple linear regression (MLR), partial least squares regression (PLSR), and principal component regression (PCR). Variable importance in the projection (VIP) scores were used to rate the importance of model input variables, indicating tryptophan as the most important variable for biomass estimation. A direct correlation between the single fluorescence signals of tryptophan and biomass was additionally set up. Results indicate a successful fitting of the MLR, PLSR, PCR, and direct tryptophan correlation models for the present case and confirm the relevance of biogenic fluorophores for bioprocess state variables monitoring. The root mean squared error of prediction (RMSEP) between the predicted and measured values for the validation batches was 0.017, 0.023, 0.025, and 0.049 g L−1 dry cell weight for MLR, PLSR, PCR, and direct tryptophan correlation, respectively. The presented approach of indirectly measuring biomass based on combined single-wavelength fluorescence measurements can be used for the development of a low-cost alternative to 2D fluorescence spectroscopy. Biotechnol. Bioeng. 2016; 113: 2394–2402. © 2016 Wiley Periodicals, Inc.     «

In this work, the evolution of different biogenic fluorophores involved in the metabolism of Pichia pastoris was determined at four different single-wavelength pairs (excitation/emission) during batch culture in microwell plates and used for effective and reliable biomass estimation by means of chemometric tools. The chemometric tools for biomass estimation were multiple linear regression (MLR), partial least squares regression (PLSR), and principal component regression (PCR). Variable importanc...     »