A Multi-Sensor Mini-Bioreactor to Preselect Silage Inoculants by Tracking Metabolic Activity in situ During Fermentation

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.

Keywords: carbon dioxide (CO2); ethanol (EtOH); fermentation; lactic acid bacteria (LAB); metabolic sensitivity; multi-sensor mini-bioreactor (MSMB); pH; silage additive.

FIGURE 1

The multi-parameter measurement system devised for selecting microbes used as silage inoculant, based on the characteristics of lactic acid bacterial (LAB) fermentation and the theory of optimal control.

FIGURE 2

A novel multi-sensor jar, manufactured according to Figure 1, is shown in cross section (A), front view (B), and top view (C) and measured in situ in the incubator (D).

FIGURE 3

Three time courses of the acidification process with respect to Lentilactobacillus buchneri (LB), LB mixed with Enterococcus faecium (LBEF), and LB mixed with Lactiplantibacillus plantarum (LBLP), under anaerobic (A) and aerobic conditions (B). LB, Lentilactobacillus buchneri; LBEF, LB mixed with Enterococcus faecium; LBLP, LB mixed with Lactiplantibacillus plantarum.

FIGURE 4

Time courses of O₂ concentration dissolved in the De Man, Rogosa, and Sharpe (MRS) and distributed in the sealed jars during the anaerobic (A) and aerobic (B) fermentations. MRS, De Man, Rogosa, and Sharpe.

FIGURE 5

Time courses of CO₂ and ethanol production with respect to LB (A), LBEF (B) and LBLP (C) under anaerobic conditions. LB, Lentilactobacillus buchneri; LBEF, LB mixed with Enterococcus faecium; LBLP, LB mixed with Lactiplantibacillus plantarum.

FIGURE 6

Relative variations of CO₂ (A) and EtOH (B) productions of the three samples.

FIGURE 7

The relationships between the pH of the three microbial samples and the differential variables of both CO₂ production (A,C,E) and EtOH production (B,D,F), where ΔCO₂ = CO₂ (t_i) − CO₂ (t_i–1) and ΔEtOH = EtOH(t_i) − EtOH(t_i–1), both calculated with t_i − t_i–1 = 2 h, i = 0, 1, …,n. The arrows denote the time course of decreasing pH.

FIGURE 8

Effect of pH on the rate of microbial respiration in relation to ΔCO₂ with pH < 5 (A), ΔCO₂ with pH ≥ 5 (B), ΔEtOH with pH < 5 (C), and ΔEtOH with pH ≥ 5 (D).

FIGURE 9

The data decomposition of the time courses of pH in Figure 3A to separate the additional roles of Enterococcus faecium (EF) or Lactiplantibacillus plantarum (LP) to support LB in accelerating the acidification process of the substrate (MRS). The solid line is the regression approximation to the separate data of LP. EF, Enterococcus faecium; LP, Lactiplantibacillus plantarum; LB, Lentilactobacillus buchneri; MRS, De Man, Rogosa, and Sharpe.