Convergence Accelerator Track J Phase 2: Rapid Detection Technologies and Decision-Support Systems for Safe, Equitable Food Systems

沙门氏菌是食源性疾病的主要原因，导致135万人感染，26500人住院，420人死亡，每年给美国经济造成41亿美元的损失。尽管在全国范围内作出了努力，但三十年来感染率一直没有变化，对弱势群体的影响不成比例，使其成为“同一个健康”问题。为了应对这一挑战，该项目与家禽业、端到端供应链、食品银行和推广教育工作者建立了多种合作伙伴关系。目标是创建一个变革性的传感器决策支持系统（DSS，称为SENS-D），该系统结合了多种快速传感技术和传感系统原型，以及可视化、预测和优化功能，以检测和减轻整个家禽供应链中的沙门氏菌污染。SENS-D的设想是提供数据驱动的解决方案，显著改善食品安全、公平、效率和抵御能力，特别是在弱势群体中。传感系统是便携式的，易于使用，准确，成本效益高。通过将传感器结果与决策支持系统相结合，这项技术将确保公平和安全的食品供应，并通过与食品安全利益攸关方的合作加以促进。SENS-D可以用于检测包括牛肉、猪肉、乳制品和农产品在内的其他食品中的各种病原体，最终减少美国食源性疾病造成的1520亿美元的经济负担。为了促进包容性和最大限度地发挥社会影响，该项目将吸引代表性不足的弱势群体、利益相关者、学生和博士后研究人员。此外，该倡议将通过在公共卫生、家禽科学、食品和动物科学、供应链、工程和分析/人工智能交叉领域为融合科学方法创造新的教育和培训机会，培训劳动力，以解决公平的食品安全问题。该项目开发了三种创新传感技术和以用户为中心的便携式系统原型，通过在10-60分钟内实现沙门氏菌血清型的快速、多重和定量检测和监测，改变了家禽检测。表面增强拉曼光谱传感器将金属纳米天线集成在侧面抛光的多模光纤芯上，能够快速定量检测沙门氏菌血清型。基于阻抗的生物传感器将沙门氏菌浓缩到可检测的阈值，捕获并识别病原体，而纳米孔促进的多位点检查点测序传感器通过单核苷酸变异来区分沙门氏菌血清型。DSS采用先进的分析和人工智能，在传感器支持的家禽供应链中监测、预测和减轻沙门氏菌的空间和时间风险。它利用基于云的One Health数据环境进行来自传感系统的实时数据集成。先进的统计和机器学习技术将预测沙门氏菌水平和产品保质期。优化将用于传感器的放置、食品的智能分配和劳动力规划，以促进传感器的实施，同时实现安全、公平、效率和弹性等多个性能指标。一个分析工具包将确定减少沙门氏菌的政策和干预措施的效力。通过利益相关者和最终用户的参与，SENS-D有可能改变沙门氏菌的缓解工作，并显著提高食品安全。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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 have been 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.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 enhance food safety.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.