Noninvasive tool for optical online monitoring of individual biomass concentrations in a defined coculture. Issue 4 (13th January 2020) - Geinitz, Bertram; Rehmann, Lars; Büchs, Jochen; Regestein, Lars
Abstract: Cocultures bear great potential in the conversion of complex substrates and process intensification, as well as, in the formation of unique components only available due to inter‐species interactions. Dynamic data of coculture composition is necessary for understanding and optimizing coculture systems. However, most standard online determined parameters measure the sum of all species in the reactor system. The kinetic behavior of the individual species remains unknown. Up to now, different offline methods are available to determine the culture composition, as well as the online measurement of fluorescence of genetically modified organisms. To avoid any genetic modification, a noninvasive online monitoring tool based on the scattered light spectrum was developed for microtiter plate cultivations. To demonstrate the potential, a coculture consisting of the bacterium Lactococcus lactis and the yeast Kluyveromyces marxianus was cultivated. Via partial least squares regression of scattered light spectra, the online determination of the individual biomass concentrations without further sampling and analyses is possible. The results were successfully validated by a Coulter counter‐analysis, taking advantage of the different cell sizes of both organisms. The findings prove the applicability of the new method to follow in detail the dynamics of a coculture. Abstract : Scattered light spectra serve as a fingerprint for different microorganisms. Thus, spectra measured online in a continuously shaken microtiter plate were used to calculate the individual biomass concentrations of cocultivated Lactococcus lactis and Kluyveromyces marxianus based on partial least squares regression. Cocultures were monitored under different cultivation conditions and were validated with an offline method, which relies on cell counting. The results prove the high potential of the presented method for the characterization and optimization of defined coculture processes.