Highlights A greywater treatment system was monitored with automated flow cytometry and turbidity. Stagnation in biological activated carbon filter led to peaks in total cell concentration and turbidity. Strong correlation was observed between TCC and turbidity. Stagnation did not lead to increase of opportunistic pathogens in the biofilter's effluent.
The management of microbial flora is a key element of the winemaking process. It impacts process time, fermentation quality (e.g., production of alcohol, development of aroma compounds, absence of undesirable microorganisms) and the overall quality of the final wine product. In microbial flora management, real-time microbial monitoring is crucial. When applying currently used classical methods (e.g., cultivation on plates) or more recent technologies (e.g., flow cytometry), the flora sampling frequency is not optimal and taking around-the-clock measurements involves high labour costs. The objective of this study was to evaluate the feasibility of automated microbial monitoring based on an online flow cytometry system for different laboratory-scale applications in the field of winemaking. Initially, a protocol for automated sampling, double-staining and analysis was validated on yeast and bacterial populations after rehydration of the starter cultures. The system was then tested on a variety of increasingly complex biological systems, simulating its applications in the winemaking process. First, a yeast starter culture preparation for “prise de mousse” was tested. Then, a mixed-culture alcoholic fermentation was monitored. Finally, a microbial-focused observation of wine aging was emulated. By overcoming specific constraints linked to the must medium (e.g., sugars, alcohol contents, production of gas, fermentation duration), the results show the potential of this technology for (1) automated yeast or bacterial monitoring in a wide range of laboratory-scale wine environments, (2) simultaneous monitoring of both total and intact populations of multiple microorganisms, (3) long observation periods, and (4) high sampling frequencies for high-resolution data. It could be particularly useful for facilitating and improving control of potential contaminants or stuck fermentations, as well as better piloting starter preparations, alcoholic fermentations, or malolactic fermentations.
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