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，26，500人住院，420人死亡，每年花费美国经济41亿美元。尽管在全国范围内作出了努力，但感染率三十年来没有变化，对弱势群体的影响不成比例，使其成为一个“同一健康”问题。为了科普这一挑战，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。我们的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），它结合了多种快速传感技术和传感系统原型，沿着可视化，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旨在提供数据驱动的解决方案，显著提高食品安全，公平，效率和弹性，特别是在弱势群体中。传感系统便携、易于使用、准确且具有成本效益。通过将传感器结果与DSS相结合，该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相关者的合作提供便利。SENS-D可以适用于检测其他食品中的各种病原体，包括牛肉，猪肉，乳制品和农产品，最终减少美国食源性疾病的1520亿美元经济负担为了促进包容性和最大化社会影响，该项目将吸引代表性不足和弱势群体，利益相关者，学生和博士后研究人员。此外，该计划将通过为公共卫生、家禽科学、食品和动物科学、供应链、工程和分析/人工智能交叉领域的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的传感技术和以用户为中心的便携式系统原型，通过实现快速、多路、并在10-60分钟内定量检测和监测沙门氏菌血清型。表面增强型拉曼光谱传感器在侧面抛光的多模光纤芯上集成了金属纳米天线，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，捕获和识别病原体，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区分沙门氏菌血清型。DSS采用先进的分析和人工智能来监测，预测和减轻沙门氏菌的风险，在空间和时间上，在传感器驱动的家禽供应链。它利用基于云的One Health数据环境进行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。优化将用于传感器放置、食物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、效率和弹性的多个性能指标。分析工具包将确定沙门氏菌缓解政策和干预措施的有效性。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并显着提高食品安全。该奖项反映了NSF的法定使命，并且通过使用基金会的知识价值和更广泛的影响进行评估，被认为值得支持审查标准。

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.