NSF Convergence Accelerator Track J: Rapid detection technologies and decision-support systems to mitigate food supply chain threats

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Salmonella is one of the leading causes of foodborne illness in the U.S. and around the world, placing a higher burden on populations of lower socioeconomic status and underrepresented racial/ethnic groups. The total cost of illnesses due to Salmonella contamination in the U.S. alone was estimated to be greater than $10.69 billion in 2018. The goal of this project is to investigate multiple transformative sensing technologies for detecting Salmonella contamination along the poultry supply chain, leading to the development of a data-driven decision-support system to improve food safety, security, equity, efficiency, and resilience. By developing multi-sectoral partnerships with the poultry industry, retail markets, food banks, and local health departments, this project brings together a multidisciplinary group of researchers across five institutions to investigate and implement an integrated sensor-enabled food supply chain decision-support system for risk assessment and Salmonella mitigation to achieve system-wide equitable food safety and better health outcomes. This technology has the potential to be adapted for the detection of other foodborne pathogens in beef, pork, dairy, and green leaf products. It may also be applied to diagnose bacterial and viral infectious diseases in clinical settings.The application of the proposed technology will ensure equitable food security for local and global consumers and reduce the economic burden of foodborne diseases, especially for vulnerable and underprivileged populations who are facing higher food security risks. The research team will work alongside multisectoral partners to address the unique needs of disadvantaged populations in food nutrition, accessibility, and equity. This project will create research and training opportunities for students to learn about the convergence science approaches at the intersection of food science, public health, animal sciences, data science, and sensing technology. The team will expand engagement with under-represented populations by providing opportunities for student research experiences, engaging researchers, partnering with the industry workforce (e.g., including immigrant workers) and multi-sectoral stakeholders, and incorporating data about underrepresented groups into the proposed system.The proposed sensing technologies are unique in terms of multiplex/simultaneous, quantitative, and selective detection, and surveillance of Salmonella serovars at low concentrations within 30 minutes assay time. This can be accomplished by developing a Surface Enhanced Raman Spectroscopy (SERS) sensor on a side polished multimode optical fiber core, which is integrated into a 3-dimensional printed microstructure at a 15-degree angle to maximize the interaction of the excitation laser with the analytes, while the nanoantenna arrays will be created using low-cost microsphere photolithography. Salmonella antigens will be detected and quantified by measuring their vibrational fingerprint SERS spectra. The project will also integrate multiple innovative features of an impedance-based biosensor on the same chip to concentrate the viral antigen sample to a detectable threshold, capture, and detect the pathogens using arrays of electrodes coated with specific antibodies to enable simultaneous and selective detection of Salmonella serovars. Instead of timely and costly whole-genome sequencing, the nanopore-facilitated, multi-locus checkpoint sequencing sensor differentiates Salmonella serovars by rapid screening a panel of single-nucleotide-variation serotyping markers distributed in one or multi-locus. By combining results from samples throughout the end-to-end food supply chain and integrating the national population-level data, the system will populate a centralized data environment to develop visualization, prediction, and optimization capabilities for microbial risk assessment and mitigation with effective and timely data-driven decision support.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.