Talanta · 2026
Low-cost microbioreactor based on 3D-printing and Arduino. Enzymatic monitoring of the water kefir fermentation using digital imaging analysis.
СтатьяНазвание и аннотация: Перевод в очереди
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Nario Nicolás A, Bernardini Santiago, Föttinger Sophie, Lang Marilina, Costa Sebastián A, Domini Claudia E, Grünhut Marcos
Аннотация
Optimizing the water kefir production process is key to ensuring reproducibility, microbiological stability, and sensory quality of the final product. Furthermore, monitoring compounds generated during the fermentation process allows for controlling the metabolic balance between bacteria and yeasts, and standardizing physicochemical and functional profiles, especially in scaling up or product development. In this work, current and accessible technologies such as 3D-printing, Arduino electronics and digital image analysis were combined to develop a portable microbioreactor (3.61 kg) for the optimization of obtaining water kefir and the development of enzymatic-based analytical methods for the quantification of metabolites of interest in this type of sample. The microbioreactor consisted of an easy-to-assemble device constructed with PLA-printed parts, which included four bioreactors made of sterilization-resistant materials (glass, PTFE, and stainless steel). Temperature and stirring speed were independently controlled in each bioreactor using Arduino electronics. The fermentation process (32 h) was monitored at different temperatures (23-43 °C) and with gentle and occasional stirring (40 rpm; 10 s every 5 min) by quantifying glucose, glycerol and lactic acid using digital image analysis with a device built with 3D-printing and using a smartphone, LED lighting and 32-well polystyrene microplate (300 μL/well), allowing the simultaneous analysis of multiple samples (32 samples). The concentration profiles obtained were as expected, and no statistically significant differences were found (p = 0.05) between the proposed methodology and UV-Vis spectroscopy. Furthermore, the greenness of the methodology was optimal (0.82), contributing to the development of sustainable methodologies aligned with the principles of green analytical chemistry.