Abstract
In the food production sector, quickly identifying potential hazards is crucial due to the resilience of many pathogens, which could lead to wasted production results and, more severely, epidemic outbreaks. E. coli monitoring is essential; however, traditional quality control methods in fish farming are often slow and intrusive, thus promoting an increase in fish stress and mortality rates. This paper presents an alternative method by utilizing a prototype inspired by polarized optical microscopy (POM), constructed with a Raspberry Pi microprocessor to assess pixel patterns and calculate analyte levels. The sensors are based on the immune complexation reactions between E. coli specific antibodies and the disruption of liquid crystal (LC) alignment, which are measured with the POM technique. The prototype yielded a sensitivity of 1.01%±0.17%/log10 (CFU/mL) for E. coli. In this paper, tests using sunlight as the prototype’s light source were also performed, and a user-friendly graphical user interface was designed.
Published Version
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