A machine vision-assisted Argonaute-mediated fluorescence biosensor for the detection of viable Salmonella in food without convoluted DNA extraction and amplification procedures

The precise identification viable pathogens hold paramount significance in the prevention of foodborne diseases outbreaks. In this study, we integrated machine vision and learning with single microsphere to develop a phage and Clostridium butyricum Argonaute (CbAgo)-mediated fluorescence biosensor for detecting viable Salmonella typhimurium (S. typhimurium) without convoluted DNA extraction and amplification procedures. Phage and lysis buffer was utilized to capture and lyse viable S. typhimurium, respectively. Subsequently, CbAgo can cleave the bacterial DNA to obtain target DNA that guides a newly targeted cleavage of fluorescent probes. After that, the resulting fluorescent signal accumulates on the streptavidin-modified single microsphere. The overall detection process is then analyzed and interpreted by machine vision and learning algorithms, achieving highly sensitive detection of S. typhimurium with a limit of detection at 40.5 CFU/mL and a linear range of 50–107 CFU/mL. Furthermore, the proposed biosensor demonstrates standard recovery rates and coefficients of variation at 93.22% − 106.02% and 1.47% − 12.75%, respectively. This biosensor exhibits exceptional sensitivity and selectivity, presenting a promising method for the rapid and effective detection of foodborne pathogens. Bacterial pathogens exist widely in the environment and seriously threaten the safety of human life. In this study, we developed a phage and Clostridium butyricum Argonaute-mediated fluorescence biosensor for the detection of viable Salmonella typhimurium in environmental water and food samples. Compared with other Salmonella detection methods, this method does not need complex DNA extraction and amplification steps, which reduces the use of chemical reagents and experimental consumables in classic DNA extraction kit methods.