Analytica chimica acta · 2026
Non-contact identification of opportunistic pathogens in mixed biofilm contaminations by hyperspectral imaging.
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Bogachev Mikhail I, Baranov Pavel S, Sinitca Aleksandr M, Mironova Anna V, Sharivzyanov Dmitry R, Basmanov Alexander A, Trizna Elena Y, Gorshkova Anna S, Pyko Nikita S, Kayumov Airat R
Аннотация
BACKGROUND: Biofilms are present on almost all surfaces in households, healthcare and medical equipment, foods, industrial and water supply systems, and often contain opportunistic pathogens that represent one of the key public health hazards. The highest risks are imposed by ESKAPEE pathogens, as well as mixed bacterial-fungal communities often exhibiting multiple drug resistance, this way challenging public healthcare.
RESULTS: Here we show how recent developments in hyperspectral imaging technology, complemented by advanced image analysis and machine learning methods, create a unique framework for the potential design of non-contact monitoring systems operating at the macroscale. We could successfully identify five key pathogenic bacteria and a common pathogenic yeast, C. albicans, that frequently occur on surfaces in monospecies and mixed biofilms consisting of combinations of various strains using hyperspectral imaging in the visible, near-infrared, and short-wave infrared spectral bands. Our results indicate that the above pathogenic species could be identified in monocultural biofilms with 95-99.5 % accuracy, while in more frequently occurring mixed biofilms obtained by combining different microorganisms, the accuracy ranges from 90 to 92 % for gram-negative E. coli, K. pneumoniae, and P. aeruginosa to 96-99 % for fungi and gram-positive E. faecalis and S. aureus, respectively, under in vitro conditions.
SIGNIFICANCE: Based on our results, we believe that hyperspectral imaging represents a promising and highly efficient technology for the rapid, regular, non-contact monitoring of various equipment and surfaces to detect bacterial and fungal pathogens in situ.
RESULTS: Here we show how recent developments in hyperspectral imaging technology, complemented by advanced image analysis and machine learning methods, create a unique framework for the potential design of non-contact monitoring systems operating at the macroscale. We could successfully identify five key pathogenic bacteria and a common pathogenic yeast, C. albicans, that frequently occur on surfaces in monospecies and mixed biofilms consisting of combinations of various strains using hyperspectral imaging in the visible, near-infrared, and short-wave infrared spectral bands. Our results indicate that the above pathogenic species could be identified in monocultural biofilms with 95-99.5 % accuracy, while in more frequently occurring mixed biofilms obtained by combining different microorganisms, the accuracy ranges from 90 to 92 % for gram-negative E. coli, K. pneumoniae, and P. aeruginosa to 96-99 % for fungi and gram-positive E. faecalis and S. aureus, respectively, under in vitro conditions.
SIGNIFICANCE: Based on our results, we believe that hyperspectral imaging represents a promising and highly efficient technology for the rapid, regular, non-contact monitoring of various equipment and surfaces to detect bacterial and fungal pathogens in situ.