AUTHOR=Shan Guilin , Rosner Victoria , Milimonka Andreas , Buescher Wolfgang , Lipski André , Maack Christian , Berchtold Wilfried , Wang Ye , Grantz David A. , Sun Yurui TITLE=A Multi-Sensor Mini-Bioreactor to Preselect Silage Inoculants by Tracking Metabolic Activity in situ During Fermentation JOURNAL=Frontiers in Microbiology VOLUME=12 YEAR=2021 URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2021.673795 DOI=10.3389/fmicb.2021.673795 ISSN=1664-302X ABSTRACT=

The microbiome in silage may vary substantially from the onset to the completion of fermentation. Improved additives and inoculants are being developed to accelerate the ensiling process, to enhance fermentation quality, and to delay spoilage during feed-out. However, current methods for preselecting and characterizing these amendments are time-consuming and costly. Here, we have developed a multi-sensor mini-bioreactor (MSMB) to track microbial fermentation in situ and additionally presented a mathematical model for the optimal assessment among candidate inoculants based on the Bolza equation, a fundamental formula in optimal control theory. Three sensors [pH, CO2, and ethanol (EtOH)] provided data for assessment, with four additional sensors (O2, gas pressure, temperature, and atmospheric pressure) to monitor/control the fermentation environment. This advanced MSMB is demonstrated with an experimental method for evaluating three typical species of lactic acid bacteria (LAB), Lentilactobacillus buchneri (LB) alone, and LB mixed with Lactiplantibacillus plantarum (LBLP) or with Enterococcus faecium (LBEF), all cultured in De Man, Rogosa, and Sharpe (MRS) broth. The fermentation process was monitored in situ over 48 h with these candidate microbial strains using the MSMB. The experimental results combine acidification characteristics with production of CO2 and EtOH, optimal assessment of the microbes, analysis of the metabolic sensitivity to pH, and partitioning of the contribution of each species to fermentation. These new data demonstrate that the MSMB associated with the novel rapid data-processing method may expedite development of microbial amendments for silage additives.