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

融合加速器轨道 J 第 2 阶段：安全、公平食品系统的快速检测技术和决策支持系统

• 批准号: 2344877
• 负责人: Mahmoud Almasri
• 金额: $ 500万
• 依托单位: University of Missouri-Columbia
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-12-15 至 2026-11-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2344877&HistoricalAwards=false
• 关键词: Convergence; Accelerator; Track; Phase; Rapid

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.

沙门氏菌是食源性疾病的主要原因，导致135万次感染，26,500次住院，420例死亡，每年造成41亿美元的经济。尽管在全国范围内进行了努力，但感染率已经不变了三十年，对脆弱人群的影响不成比例，这使其成为“一个健康”问题。为了应对这一挑战，该项目与家禽业，端到端的供应链，食品银行和推广教育者建立了潜水员的伙伴关系。目的是创建一个具有变革性传感器的决策支持系统（DSS；称为SENS-D），该系统结合了多种快速灵敏度技术和灵敏度系统原型，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。设想Sens-D可以提供数据驱动的解决方案，从而显着提高食品安全，公平性，效率和韧性，尤其是在受到干扰人群中。传感器系统是便携式，易于使用，准确且具有成本效益的。通过将传感器结果与DSS集成，该技术将确保通过与食品安全利益相关者的合作准备公平且安全的食品供应。 Sens-D可以适应于其他食品中的各种病原体，包括牛肉，猪肉，乳制品和农产品，最终减少了美国食源性疾病的1520亿美元经济烧伤，以促进包容性和最大化社会影响，该项目将与人数不足和易受伤害的群体，利益相关者，学生，学生和后者研究人员参与。此外，该举措将通过创建新的教育和培训机会来培训劳动力，以在公共卫生，家禽科学，食品和动物科学，供应链，工程和分析/AI中创建新的教育和培训机会，以进行收敛科学方法，以开发三种创新的传感器技术，并开发出量子量的快速量，并开发出端口的量子，并开发了端口量的快速范围，从在10-60分钟内监测沙门氏菌血清射手。表面增强的拉曼光谱传感器在侧面涂层的多模光纤芯上整合了金属纳米antennas，从而可以快速，定量检测沙门氏菌血清射手。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，而纳米核酸元素的多层次检查点测序传感器通过单核苷酸变化区分了沙门氏菌血清。 DSS员工先进的分析和AI，以在启用传感器的派供应链中监测，预测和减轻沙门氏菌风险。它利用基于云的一个健康数据环境从灵敏度系统中进行实时数据集成。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，智能分配食品以及计划实施传感器的劳动力，同时实现安全性，公平性，效率和弹性的多重性能指标。分析工具包将确定沙门氏菌缓解的政策和干预措施的效率。通过利益相关者和最终用户参与，Sens-D有可能改变沙门氏菌的缓解并大大提高食品安全性。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子和更广泛的影响评估标准来通过评估来表现出珍贵的支持。