Anna-Lena Heins, Thomas Skov, Krist V. Gernaey, Bekzod Khakimov, Charlotte Schöller, Anders Clausen, Lene D. Christiansen, Søren B. Engelsen, Klavs M. Sørensen
An industrial scale biomass production using batch or fed-batch fermentations usually optimized by selection of bacterial strains, tuning fermentation media, feeding strategy, and temperature. However, in-depth investigation of the biomass metabolome during the production may reveal new knowledge for better optimization. In this study, for the first time, the authors investigated seven fermentation batches performed on five Streptoccoccus thermophilus strains during the biomass production at Chr. Hansen (Denmark) in a real life large scale fermentation process. The study is designed to investigate effects of batch fermentation, fermentation time, production line, and yeast extract brands on the biomass metabolome using untargeted GC-MS metabolomics. Processing of the raw GC-MS data using PARAFAC2 revealed a total of 90 metabolites out of which 64 are identified. Partitioning of the data variance according to the experimental design was performed using ASCA and revealed that batch and fermentation time effects and their interaction term were the most significant effects. The yeast extract brand had a smaller impact on the biomass metabolome, while the production line showed no effect. This study shows that in-depth metabolic analysis of fermentation broth provides a new tool for advanced optimization of high-volume-low-cost biomass production by lowering the cost, increase the yield, and augment the product quality.
Untargeted GC-MS metabolomics reveals the dynamics of metabolites in the Streptoccoccus thermophilus fermentation broth as a function of fermentation time, batch, and yeast extract substrate. The study is designed to investigate effects of batch fermentation, fermentation time, production line, and yeast extract brands on the biomass metabolome using untargeted GC-MS metabolomics. Processing of the raw GC-MS data using PARAFAC2 revealed 90 metabolites out of which 64 are identified. ASCA based partitioning of the data variance according to the experimental design showed that batch, fermentation time, and their interaction term were the most significant effects. The yeast extract brand had smaller impact on the biomass metabolome, while production line showed no effect.
