OPTICAL CRISPR NANO-DIAGNOSTICS AGAINST COMMON SALMONELLA SEROTYPES USING MOBILE-APP DRIVEN IMAGE ANALYSIS AND DATA TRANSMISSION

Foodborne pathogens are the main source of foodborne illnesses and cause significant threat to public health and global economy. Without timely intervention, these pathogens can completely disrupt the supply chain for critical foodstuffs and consequently elevate economic stress and public anxiety. Among all the leading foodborne pathogens, Salmonella has proven to be the costliest ($3.7 billion/year) to the US economy. In this proposal, our goal is to develop an innovative nano-diagnostic approach for Salmonella Enteritidis and Salmonella Typhimurium, which are the most prevalent serotypes and account for half of all human infections in the US.The objectives of this proposal are: (1) Ultra-sensitive fluorometric detection of Salmonella contamination in food products using a benchtop spectrofluorometer. (2) Translation of the fluorescence detection into instrument-free visual Salmonella diagnostics using nanoparticles, and (3) Machine-learning assisted optical image analysis/classification and data transmission through lab-built mobile-app.The proposed approach offers the detection of Salmonella under 6-hr; surpassing the current microbial culturing method which can take up to 5 days. This is important for rapid and routine sampling to avoid potential cross-contamination or the spread of Salmonella. The data analysis through image-classification and mobile-app facilitated data transmission is important for taking immediate actions and the containment of disease transmission, thus preventing further health risks and economic loss. The proposed simple-to-use optical nano-diagnostic setup, as proposed here, offers a universal diagnostic template which can be employed beyond Salmonella detection. Therefore, there is potential for broader applications once the proposed diagnostic is realized.