An improved dynamic method to measure kL a in bioreactors.

An accurate measurement or estimation of the volumetric mass transfer coefficient kL a is crucial for the design, operation, and scale up of bioreactors. Among different physical and chemical methods, the classical dynamic method is the most widely applied method to simultaneously estimate both kL a and cell's oxygen utilization rate. Despite several important follow-up articles to improve the original dynamic method, some limitations exist that make the classical dynamic method less effective under certain conditions. For example, for the case of high cell density with moderate agitation, the dissolved oxygen concentration barely increases during the re-gassing step of the classical dynamic method, which makes kL a estimation impossible. To address these limitations, in this work we present an improved dynamic method that consists of both an improved model and an improved procedure. The improved model takes into account the mass transfer between the headspace and the broth; in addition, nitrogen is bubbled through the broth when air is shut off. The improved method not only enables a faster and more accurate estimation of kL a, but also allows the measurement of kL a for high cell density with medium/low agitation that is impossible with the classical dynamic method. Scheffersomyces stipitis was used as the model system to demonstrate the effectiveness of the improved method; in addition, experiments were conducted to examine the effect of cell density and agitation speed on kL a.

A novel soft sensor approach for estimating individual biomass in mixed cultures.

Due to many advantages associated with mixed cultures, their application in biotechnology has expanded rapidly in recent years. At the same time, many challenges remain for effective mixed culture applications. One obstacle is how to efficiently and accurately monitor the individual cell populations. Current approaches on individual cell mass quantification are suitable for off-line, infrequent characterization. In this study, we propose a fast and accurate "soft sensor" approach for estimating individual cell concentrations in mixed cultures. The proposed approach utilizes optical density scanning spectrum of a mixed culture sample measured by a spectrophotometer over a range of wavelengths. A multivariate linear regression method, partial least squares or PLS, is applied to correlate individual cell concentrations to the spectrum. Three experimental case studies are used to examine the performance of the proposed soft sensor approach. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:347-354, 2017.

Impact of pseudo-continuous fermentation on the ethanol tolerance of Scheffersomyces stipitis.

In this work we conducted the pseudo-continuous fermentation, i.e., continuous fermentation with cell retention, using Scheffersomyces stipitis, and studied its effect on ethanol tolerance of the strain. During the fermentation experiments, S. stipitis was adapted to a mild concentration of ethanol (20-26 g/L) for two weeks. Two substrates (glucose and xylose) were used in different fermentation experiments. After fermentation, various experiments were performed to evaluate the ethanol tolerance of adapted cells and unadapted cells. Compared to the unadapted cells, the viability of adapted cells increased by 8 folds with glucose as the carbon source and 6 folds with xylose as the carbon source following exposure to 60 g/L ethanol for 2 h. Improved ethanol tolerance of the adapted cells was also revealed in the effects of ethanol on plasma membrane permeability, extracellular alkalization and acidification. The mathematical modeling of cell leakage, extracellular alkalization and acidification revealed that cells cultured on glucose show better ethanol tolerance than cells cultured on xylose but the differences become smaller for adapted cells. The results show that pseudo-continuous fermentation can effectively improve cell's ethanol tolerance due to the environmental pressure during the fermentation process.