Title

Rapid Detection Technologies and Decision Support System for Safe, Equitable Food Systems

PART 1:

Salmonella is a leading cause of foodborne illness, resulting in 1.35 million infections, 26,500 hospitalizations, 420 deaths, and costs the US economy $4.1 billion annually. Despite nationwide efforts, the infection rates are unchanged for three decades, disproportionately affecting vulnerable populations, making it a “One Health” issue. To cope with this challenge, this project establishes diverse partnerships with the poultry industry, end-to-end supply chains, food banks, and Extension educators. The objective is to create a transformative sensor-enabled decision support system (DSS; termed as SENS-D), which incorporates multiple rapid sensing technologies and sensing systems prototypes, along with visualization, prediction, and optimization capabilities to detect and mitigate Salmonella contamination throughout the poultry supply chain. SENS-D is envisioned to provide data-driven solutions that significantly improve food safety, equity, efficiency, and resilience, particularly among disadvantaged populations. The sensing systems are portable, easy-to-use, accurate, and cost-effective. By integrating sensor results with the DSS, this technology will ensure an equitable and secure food supply, facilitated through collaborations with food safety stakeholders. SENS-D can be adapted to detect various pathogens in other food products including beef, pork, dairy, and produce, ultimately reducing the $152 billion economic burden of foodborne illness in the U.S. To promote inclusivity and maximizing social impact, the project will engage underrepresented and vulnerable groups, stakeholders, students and postdoctoral researchers. Additionally, this initiative will train the workforce to tackle equitable food safety by creating new educational and training opportunities for convergence science approaches at the intersection of Public Health, Poultry Science, Food and Animal Science, Supply Chain, Engineering, and Analytics/AI. 

PART 2:

This project develops three innovative sensing technologies and user-centric prototypes for portable systems, transforming poultry testing by enabling rapid, multiplex, and quantitative detection and surveillance of Salmonella serovars within 10-60 minutes. The Surface Enhanced Raman Spectroscopy sensor integrates metal nanoantennas on a side-polished multimode optical fiber core, enabling rapid, quantitative detection of Salmonella serovars. The impedance-based biosensor concentrates Salmonella to a detectable threshold, capturing and identifying the pathogen, while the nanopore-facilitated, multi-locus checkpoint sequencing sensor differentiates Salmonella serovars through single-nucleotide variations. The DSS employs advanced analytics and AI to monitor, predict, and mitigate Salmonella risks, both spatially and temporally, in a sensor-enabled poultry supply chain. It utilizes a cloud based One Health data environment for real-time data integration from the sensing system. Advanced statistical and machine learning techniques will predict Salmonella levels and product shelf-life. Optimization will be used for sensor placement, intelligent distribution of food, and workforce planning to facilitate implementation of sensors, while achieving multiple performance metrics of safety, equity, efficiency, and resilience. An analytical toolkit will determine the efficacy of policies and interventions for Salmonella mitigation. Through stakeholder and end-user engagement, SENS-D has the potential to transform Salmonella mitigation and significantly enhancing food safety.