Real-time Detection of Foodborne Pathogens and Biofilm in the Food Processing Environment with Bactiscan, A Macro-scale Fluorescence Device
2025
Callum Highmore | Kirsty Cooper | Joe Parker | Joshua Robinson | Roberto Castangia | Jeremy S. Webb
Food safety relies on rapid detection methods and rigorous sampling of the food processing environment and is challenged by recurrent biofilm contamination and by sublethally injured bacteria that can evade detection. Bactiscan is investigated as an alternative detection approach, a macro-scale and reagentless device that detects microbial contamination through activating the green fluorescence of glycoproteins in the bacterial cell wall. The detection capability of Bactiscan was tested on foodborne pathogens Escherichia coli, Listeria monocytogenes, Salmonella enterica, and Staphylococcus aureus. Detection by Bactiscan was assessed using 3 independent observers viewing bacterial samples dried on stainless steel, using 3 biological repeats and 5 technical repeats. Detection by Bactiscan was possible to 1.20 * 106 colony forming units (CFU), compared to 1.36 * 104 CFU by ATP swab testing, where Bactiscan detection limits were defined by the concentration at which 50% of the samples were observed under illumination of the device. Heat-killed and chlorine-stressed E. coli and S. enterica caused a 2-log reduction in detection by ATP swab tests (p ≤ 0.05), while detection by Bactiscan was unaffected (p ≥ 0.05). Pathogen biofilms were detectable via Bactiscan with >80% accuracy at 4 days of growth; E. coli and L. monocytogenes biofilms were visible at 2 days of growth. In situ contamination studies determined that Bactiscan can detect microbial contamination on chicken, salmon, and yoghurt samples with stronger fluorescence than a competitor’s UV torch. The presence of one of the pathogens on the food samples was confirmed by metagenome sequencing, determining that S. aureus was present in 7 samples out of 9 with a relative abundance of >0.5%. These data demonstrate that Bactiscan can effectively detect bacteria present in the food processing environment and can complement existing technologies to improve food industry cleaning practices and infection prevention.
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